Irrigation with permeates to upgrade the quality of red pepper: a case study in Arava region, Israel.

Management of environmental resources presents challenges across agricultural production. In the case of the semi-arid region of Arava Valley in Israel, irrigation with groundwater brackish water is a widespread practice that has severe limitations. In this research studies are taking place for brackish groundwater upgrading for unrestricted use for the irrigation and sustainable agricultural production. The treatment system applies two main treatment stages: nanofiltration and reverse osmosis membrane process and, the pilot system with a capacity of around 1 m3/hr. Different mixtures of nanofiltration and reverse osmosis, Brackish and potable permeates are then applied for the irrigation of pepper crops. The field results with the graphical visualization approach using total ranking, multi-dimensional scaling, and clustering technique show that the conventional brackish feed water produces good results of relaxation time, total soluble solids and Vitamin C content. Treatment system using hybrid nanofiltration and reverse osmosis technology improves product quality related to the relative fruit colour red shade.

Low-Resolution Raman Spectroscopy for the detection of contaminant species in algal bioreactors.

Fouling of aquatic systems by harmful microalgal and cyanobacterial species is an environmental and public health concern. Microalgal bioreactors are engineered ecosystems for the cultivation of algal biomass to meet the increasing demand for alternative protein sources and algae-derived products. Such bioreactors are often open or semi-open ponds or raceways that are prone to contamination by contaminant photosynthetic microorganisms, including harmful cyanobacterial species (HCBs). HCBs affect the quality of products through the accumulation of off-flavours, reducing their acceptance by consumers, and through the production of several different toxins collectively known as cyanotoxins. The density of cultured species within the bioreactor environment creates difficulty in detecting low concentrations of contaminant cells, and there is currently no technology enabling rapid monitoring of contaminations. The present study demonstrates the potential of Low-Resolution Raman Spectroscopy (LRRS) as a tool for rapid detection of low concentrations of HCBs within dense populations of the spirulina (Arthrospira platensis) cultures. An LRRS system adapted for the direct measurement of raw biomass samples was used to assemble a database of Raman spectral signatures, from eight algal and cyanobacterial strains. This dataset was used to develop both quantitative and discriminative chemometric models. The results obtained from the chemometric analyses demonstrate the ability of the LRRS to detect and quantify algal and cyanobacterial species at concentrations as low as 103 cells/mL and to robustly discriminate between species at concentrations of 104 cells/mL. The LRRS and chemometric analyses were further able to detect the presence of low concentrations (103cells/mL) of contaminating species, including the toxic cyanobacterium Microcystis aeruginosa, within dense (>107 cells/mL) spirulina cultures. The results presented provide a first demonstration of the potential of LRRS technology for real-time detection of contaminant species within microalgal bioreactors, and possibly for early detection of developing harmful algal blooms in other aquatic ecosystems.

A novel approach for long-term spectral monitoring of desert shrubs affected by an oil spill.

Crude oil pollution is a global environmental concern since it persists in the environment longer than most conventional carbon sources. In December 2014, the hyper-arid Evrona Nature Reserve, Israel, experienced large-scale contamination when crude oil spilled. The overarching goal of the study was to investigate the possible changes, caused by an accidental crude oil spill, in the leaf reflectance and biochemical composition of four natural habitat desert shrubs. The specific objectives were (1) to monitor the biochemical properties of dominant shrub species in the polluted and control areas; (2) to study the long-term consequences of the contamination; (3) to provide information that will assist in planning rehabilitation actions; and (4) to explore the feasibility of vegetation indices (VIs), along with the machine learning (ML) technique, for detecting stressed shrubs based on the full spectral range. Four measurement campaigns were conducted in 2018 and 2019. Along with the various stress indicators, field spectral measurements were performed in the range of 350-2500 nm. A regression analysis to examine the relation of leaf reflectance to biochemical contents was carried out, to reveal the relevant wavelengths in which polluted and control plants differ. Vegetation indices applied in previous studies were found to be less sensitive for indirect detection of long-term oil contamination. A novel spectral index, based on indicative spectral bands, named the "normalized blue-green stress index" (NBGSI), was established. The NBGSI distinguished significantly between shrubs located in the polluted and in the control areas. The NBGSI showed a strong linear correlation with pheophytin a. Machine learning classification algorithms obtained high overall prediction accuracy in distinguishing between shrubs located in the oil-polluted and the control sites, indicating internal component differences. The findings of this study demonstrate the efficacy of indirect and non-destructive spectral tools for detecting and monitoring oil pollution stress in shrubs.

Hyperspectral Technologies for Assessing Seed Germination and Trifloxysulfuron-methyl Response in Amaranthus palmeri (Palmer Amaranth).

Weed infestations in agricultural systems constitute a serious challenge to agricultural sustainability and food security worldwide. Amaranthus palmeri S. Watson (Palmer amaranth) is one of the most noxious weeds causing significant yield reductions in various crops. The ability to estimate seed viability and herbicide susceptibility is a key factor in the development of a long-term management strategy, particularly since the misuse of herbicides is driving the evolution of herbicide response in various weed species. The limitations of most herbicide response studies are that they are conducted retrospectively and that they use in vitro destructive methods. Development of a non-destructive method for the prediction of herbicide response could vastly improve the efficacy of herbicide applications and potentially delay the evolution of herbicide resistance. Here, we propose a toolbox based on hyperspectral technologies and data analyses aimed to predict A. palmeri seed germination and response to the herbicide trifloxysulfuron-methyl. Complementary measurement of leaf physiological parameters, namely, photosynthetic rate, stomatal conductence and photosystem II efficiency, was performed to support the spectral analysis. Plant response to the herbicide was compared to image analysis estimates using mean gray value and area fraction variables. Hyperspectral reflectance profiles were used to determine seed germination and to classify herbicide response through examination of plant leaves. Using hyperspectral data, we have successfully distinguished between germinating and non-germinating seeds, hyperspectral classification of seeds showed accuracy of 81.9 and 76.4%, respectively. Sensitive and resistant plants were identified with high degrees of accuracy (88.5 and 90.9%, respectively) from leaf hyperspectral reflectance profiles acquired prior to herbicide application. A correlation between leaf physiological parameters and herbicide response (sensitivity/resistance) was also demonstrated. We demonstrated that hyperspectral reflectance analyses can provide reliable information about seed germination and levels of susceptibility in A. palmeri. The use of reflectance-based analyses can help to better understand the invasiveness of A. palmeri, and thus facilitate the development of targeted control methods. It also has enormous potential for impacting environmental management in that it can be used to prevent ineffective herbicide applications. It also has potential for use in mapping tempo-spatial population dynamics in agro-ecological landscapes.