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Intrinsic and Ionic Conduction in Humidity-Sensitive Sulfonated Polyaniline
Doan, D.C.T. ; Ramaneti, R. ; Baggerman, J. ; Tong, H.D. ; Marcelis, A.T.M. ; Rijn, C.J.M. van - \ 2014
Electrochimica Acta 127 (2014). - ISSN 0013-4686 - p. 106 - 114.
acid-doped polyaniline - poly(vinyl alcohol) - charge-transfer - electrical-conductivity - impedance spectroscopy - polymer electrolytes - gaseous substances - water-absorption - sensors - films
The influence of humidity on the conductivity of sulfonated polyaniline (SPANI) and polyaniline (PANI) is investigated with electrochemical impedance spectroscopy (EIS). Separation of intrinsic (q) and ionic charge (i) mobility was observed using combination of ac and dc impedance measurements at relative humidity concentrations (RH) ranging from 5% to 90%. Nyquist plots for both acid-doped PANI and SPANI show single semicircles at all humidity levels, indicating pure intrinsic charge conduction. These semicircles are well-described with a fairly constant capacitance (C1 = 10-10 F) of the interdigitated electrodes and with a large varying resistance (10 kO - 10 MO) of the polymer bulk with RC times in the order of 10-3-10-6 sec. In all samples the intrinsic charge conductivity increases for increasing humidity, with emeraldine base PANI showing the strongest (2 orders of magnitude) increase upon raising the RH from 5 - 90%. A partial exchange of protons with sodium ions in SPANI (SPAN-Na) induces a second semicircle in the Nyquist plot at humidity levels above 40% with large RC (t) values (10-1 - 10-3 sec), which is attributed to a contribution of ionic mobility at the polymer/electrode interface. Blends of SPAN-Na with poly(vinyl alcohol) (PVA) and polyacrylamide (PAA) also show a second semicircle at slightly higher humidity levels from 50% RH onwards. In all cases this second semicircle is best described by a Constant Phase Element (CPE) with a value (order typical 10-9 - 10-7 F) increasing at higher humidity, and a varying exponential coefficient (0.5 <n <0.8), possibly due to a changing dispersion in the representative relaxation times for migration and accumulation of the ionic species. In addition, for SPAN-Na/SPANI samples blended with PVA a third semicircle is seen which can be described by an additional RC element representing a second phase or grain boundary contribution in the film.
Invited review: Sensors to support health management on dairy farms
Rutten, C.J. ; Velthuis, A.G.J. ; Steeneveld, W. ; Hogeveen, H. - \ 2013
Journal of Dairy Science 96 (2013)4. - ISSN 0022-0302 - p. 1928 - 1952.
automatic milking systems - clinical mastitis detection - economic decision-making - lactating holstein cows - somatic-cell count - estrus detection - electrical-conductivity - bovine-milk - subclinical mastitis - ruminal ph
Since the 1980s, efforts have been made to develop sensors that measure a parameter from an individual cow. The development started with individual cow recognition and was followed by sensors that measure the electrical conductivity of milk and pedometers that measure activity. The aim of this review is to provide a structured overview of the published sensor systems for dairy health management. The development of sensor systems can be described by the following 4 levels: (I) techniques that measure something about the cow (e.g., activity); (II) interpretations that summarize changes in the sensor data (e.g., increase in activity) to produce information about the cow’s status (e.g., estrus); (III) integration of information where sensor information is supplemented with other information (e.g., economic information) to produce advice (e.g., whether to inseminate a cow or not); and (IV) the farmer makes a decision or the sensor system makes the decision autonomously (e.g., the inseminator is called). This review has structured a total of 126 publications describing 139 sensor systems and compared them based on the 4 levels. The publications were published in the Thomson Reuters (formerly ISI) Web of Science database from January 2002 until June 2012 or in the proceedings of 3 conferences on precision (dairy) farming in 2009, 2010, and 2011. Most studies concerned the detection of mastitis (25%), fertility (33%), and locomotion problems (30%), with fewer studies (16%) related to the detection of metabolic problems. Many studies presented sensor systems at levels I and II, but none did so at levels III and IV. Most of the work for mastitis (92%) and fertility (75%) is done at level II. For locomotion (53%) and metabolism (69%), more than half of the work is done at level I. The performance of sensor systems varies based on the choice of gold standards, algorithms, and test sizes (number of farms and cows). Studies on sensor systems for mastitis and estrus have shown that sensor systems are brought to a higher level; however, the need to improve detection performance still exists. Studies on sensor systems for locomotion problems have shown that the search continues for the most appropriate indicators, sensor techniques, and gold standards. Studies on metabolic problems show that it is still unclear which indicator reflects best the metabolic problems that should be detected. No systems with integrated decision support models have been found. Key words: automated
Pesticide transport pathways from a sloped litchi orchard to an adjacent tropical stream as identified by hydrograph separation
Duffner, A. ; Ingwersen, J. ; Hugenschmidt, C. ; Streck, T. - \ 2012
Journal of Environmental Quality 41 (2012)4. - ISSN 0047-2425 - p. 1315 - 1323.
climate-soil controls - open-fractured soil - hydrological processes - northern thailand - electrical-conductivity - conceptual examination - mountainous catchment - headwater catchment - preferential flow - isotopic tracers
This study was performed to identify the transport pathways of pesticides from a sloped litchi (Litchi chinensis Sonn.) orchard to a nearby stream based on a three-component hydrograph separation (baseflow, interflow, surface runoff). Dissolved silica and electrical conductivity were chosen as representative tracers. During the study period (30 d), 0.4 and 0.01% of the applied mass of atrazine and chlorpyrifos, respectively, were detected in the stream after 151 mm of rainfall. Baseflow (80–96%) was the dominant hydrological flow component, followed by interflow (3–18%) and surface runoff (1–7%). Despite its small contribution to total discharge, surface runoff was the dominant atrazine transport pathway during the first days after application because pesticide concentrations in the surface runoff flow component declined quickly within several days. Preferential transport with interflow became the dominant pathway of atrazine. Because chlorpyrifos was detected in the stream water only twice, it was not included in the hydrograph separation. A feature of the surface runoff pathway was the coincidence of pesticide and discharge peaks. In contrast, peak concentrations of pesticides transported by interflow occurred during the hydrograph recession phases. Stormflow generation and pesticide transport depended on antecedent rainfall. The combination of high-resolution pesticide concentration measurements with a three-component hydrograph separation has been shown to be a suitable method to identify pesticide transport pathways under tropical conditions.
The arable farmer as the assessor of within-field soil variation
Heijting, S. ; Bruin, S. de; Bregt, A.K. - \ 2011
Precision Agriculture 12 (2011)4. - ISSN 1385-2256 - p. 488 - 507.
management zones - electrical-conductivity - precision agriculture - calcium-chloride - knowledge - information - variability - maps - community
Feasible, fast and reliable methods of mapping within-field variation are required for precision agriculture. Within precision agriculture research much emphasis has been put on technology, whereas the knowledge that farmers have and ways to explore it have received little attention. This research characterizes and examines the spatial knowledge arable farmers have of their fields and explores whether it is a suitable starting point to map the within-field variation of soil properties. A case study was performed in the Hoeksche Waard, the Netherlands, at four arable farms. A combination of semi-structured interviews and fieldwork was used to map spatially explicit knowledge of within-field variation. At each farm, a field was divided into internally homogeneous units as directed by the farmer, the soil of the units was sampled and the data were analysed statistically. The results show that the farmers have considerable spatial knowledge of their fields. Furthermore, they apply this knowledge intuitively during various field management activities such as fertilizer application, soil tillage and herbicide application. The sample data on soil organic matter content, clay content and fertility show that in general the farmers’ knowledge formed a suitable starting point for mapping within-field variation in the soil. Therefore, it should also be considered as an important information source for highly automated precision agriculture systems.
Somatic cell count assessment at the quarter or cow milking level
Mollenhorst, H. ; Tol, P.P.J. van der; Hogeveen, H. - \ 2010
Journal of Dairy Science 93 (2010). - ISSN 0022-0302 - p. 3358 - 3364.
operating characteristic curves - electrical-conductivity - subclinical mastitis - fuzzy-logic
The aim was to investigate whether on-line somatic cell count (SCC) assessment, when combined with electrical conductivity (EC), should be implemented at the udder quarter or at the cow level. Data were collected from 3 farms with automatic milking systems, resulting in 3,191 quarter milkings used in the analyses. Visual observations of foremilk and quarter milk samples for laboratory SCC analysis were used to define 2 gold standards. One was based on visual observation only and the other was based on a combination of visual observation and SCC (using a reference value of 500,000 cells/mL), which means that a quarter milking must have visually abnormal milk as well as an increased SCC to be categorized positive. On-line SCC assessment took place at the quarter level during the first part of the milking. Composite cow level samples were used for laboratory SCC analysis and to compare the performance of SCC assessment at quarter and cow levels. The EC at the quarter level was measured by in-line sensors of the automatic milking system. Alerts for SCC indicators were calculated based on straightforward reference values. Alerts for EC were based on straightforward reference values, or on interquarter ratios. The latter was calculated by dividing the value of a given quarter by the average value of the 2 lowest quarters of that milking. The EC and SCC indicators were combined with either a Boolean “and” or “or” function. Receiver operating characteristic curves were used to visually present results using different threshold values. Sensitivity, specificity, and success rate at the quarter level and false alert rate per 1,000 cow milkings were used to compare indicators at given sensitivity or specificity levels. Quarter level SCC assessment was superior to cow level assessment (transformed partial area under the curve = 0.70 vs. 0.62) when combined with EC measurement at quarter level. When aiming for the same sensitivity level (e.g., 50%) with all visual abnormal milk as the gold standard, more false alerts were generated with cow level assessment (137 per 1,000 cow milkings) compared with quarter level SCC assessment (75 per 1,000 cow milkings). As a comparison, using EC alone resulted in 292 false alerts per 1,000 cow milkings in the same situation. Therefore, it is concluded that quarter level SCC assessment was superior to cow level assessment when combined with EC measurement at quarter level. Key words: abnormal milk; detection; on-line assessment; somatic cell count
Sensors and Clinical Mastitis-The Quest for the Perfect Alert
Hogeveen, H. ; Kamphuis, Claudia ; Steeneveld, W. ; Mollenhorst, H. - \ 2010
Sensors 10 (2010)9. - ISSN 1424-8220 - p. 7991 - 8009.
automatic milking systems - dynamic light-scattering - somatic-cell count - dairy-cows - electrical-conductivity - bovine mastitis - neural-networks - detection model - abnormal milk - fuzzy-logic
When cows on dairy farms are milked with an automatic milking system or in high capacity milking parlors, clinical mastitis (CM) cannot be adequately detected without sensors. The objective of this paper is to describe the performance demands of sensor systems to detect CM and evaluats the current performance of these sensor systems. Several detection models based on different sensors were studied in the past. When evaluating these models, three factors are important: performance (in terms of sensitivity and specificity), the time window and the similarity of the study data with real farm data. A CM detection system should offer at least a sensitivity of 80% and a specificity of 99%. The time window should not be longer than 48 hours and study circumstances should be as similar to practical farm circumstances as possible. The study design should comprise more than one farm for data collection. Since 1992, 16 peer-reviewed papers have been published with a description and evaluation of CM detection models. There is a large variation in the use of sensors and algorithms. All this makes these results not very comparable. There is a also large difference in performance between the detection models and also a large variation in time windows used and little similarity between study data. Therefore, it is difficult to compare the overall performance of the different CM detection models. The sensitivity and specificity found in the different studies could, for a large part, be explained in differences in the used time window. None of the described studies satisfied the demands for CM detection models
Decision-tree induction to detect clinical mastitis with automatic milking
Kamphuis, C. ; Mollenhorst, H. ; Feelders, A. ; Pietersma, D. ; Hogeveen, H. - \ 2010
Computers and Electronics in Agriculture 70 (2010). - ISSN 0168-1699 - p. 60 - 68.
electrical-conductivity - detection model - roc curve - system - estrus - cows
a b s t r a c t This study explored the potential of using decision-tree induction to develop models for the detection of clinical mastitis with automatic milking. Sensor data (including electrical conductivity and colour) of over 711,000 quarter milkings were collected from December 2006 till August 2007 at six Dutch dairy herds milking automatically. Farmer recordings of quarter milkings with visible signs of mastitis were considered as gold standard positive cases (n = 97), quarter milkings that were recorded as being visually normal as gold standard negatives (n = 339). Randomly chosen quarter milkings that were not visually checked, that were outside a 2-week range before or after a gold standard positive case and that were not manually or automatically separated were added to end up with 3000 gold standard negatives. Decision trees, with varying confidence factors and cost matrices to study their effect on performance characteristics, were developed with the probability of having clinical mastitis for each quarter milking as output. Detection performance of decision trees was estimated using 10-fold cross-validation. Evaluated performance characteristics were the sensitivity and specificity, both calculated at a threshold value of 0.50 for the probability estimate for clinical mastitis. The transformed partial area under the curve was used to summarise the diagnostic ability of decision trees within a specified range of interest (specificity =97%). Receiver operating characteristic curves visualized all combinations of sensitivity and specificity of decision trees within this range. Results showed that decision trees are easy to interpret when visualised. The lower the confidence factor, the smaller the decision trees: a cost insensitive decision tree with a confidence factor of 0.05 needed only eleven test nodes to classify all 3097 records with a sensitivity of 23.7% and a specificity of 99.2%. The decision tree with default parameter settings showed a transformed partial area under the curve value of 0.6420. By introducing costs for false negative classifications this value increased to 0.6476. At a specificity level of 99%, the decision tree with the highest transformed partial area under the curve value showed a sensitivity of 29.8%. Detection performances of the different decision trees were comparable with those of models currently used by automatic milking systems. As it was possible to achieve these results with the use of a rather simple decision tree algorithm, we believe that decision tree induction shows potential for detecting clinical mastitis with automatic milking.
Mastitis diagnostics and performance monitoring: a practical approach
Lam, T.J.G.M. ; Olde Riekerink, R. ; Sampimon, O.C. ; Smith, H.E. - \ 2009
Irish Veterinary Journal 62 (2009)Supl.4. - ISSN 0368-0762 - p. 34 - 39.
somatic-cell count - polymerase-chain-reaction - bovine mastitis - milk samples - intramammary infections - staphylococcus-aureus - dairy-cattle - streptococcus-agalactiae - electrical-conductivity - clinical mastitis
In this paper a review is given of frequently used mastitis diagnostic methods in modern dairy practice. Methods used at the quarter, cow, herd and regional or national level are discussed, including their usability for performance monitoring in udder health. Future developments, such as systems in which milk-derived parameters are combined with modern analytical techniques, are discussed. It is concluded that, although much knowledge is available and science is still developing and much knowledge is available, it is not always fully exploited in practice.
Tomato nitrogen accumulation and fertilizer use efficiency on a sandy soil, as affected by nitrogen rate and irrigation scheduling
Zotarelli, L. ; Dukes, M.D. ; Scholberg, J.M.S. ; Munoz-Carpena, R. ; Icerman, J. - \ 2009
Agricultural Water Management 96 (2009)8. - ISSN 0378-3774 - p. 1247 - 1258.
water-use efficiency - drip irrigation - electrical-conductivity - processing tomato - nitrate - growth - root - fertigation - system - yield
Tomato production systems in Florida are typically intensively managed with high inputs of fertilizer and irrigation and on sandy soils with low inherent water and nutrient retention capacities; potential nutrient leaching losses undermine the sustainability of such systems. The objectives of this 3-year field study were to evaluate the interaction between N-fertilizer rates and irrigation scheduling on crop N and P accumulation, N-fertilizer use efficiency (NUE) and NO3-N leaching of tomato cultivated in a plastic mulched/drip irrigated production system in sandy soils. Experimental treatments were a factorial combination of three irrigation scheduling regimes and three N-rates (176, 220, and 330 kg ha(-1)). Irrigation treatments included were: (1) surface drip irrigation (SUR) both the irrigation and fertigation line placed underneath the plastic mulch; (2) subsurface drip irrigation (SDI) where the irrigation drip was placed 0.15 m below the fertigation line which was located on top of the bed; and (3) TIME (conventional control) with the irrigation and fertigation lines placed as in SUR and irrigation applied once a day. Except for the TIME treatment all irrigation treatments were soil moisture sensor (SMS)based with irrigation occurring at 10% volumetric water content. Five irrigation windows were scheduled daily and events were bypassed if the soil water content exceeded the established threshold. The use of SMS-based irrigation systems significantly reduced irrigation water use, volume percolated, and nitrate leaching. Based on soil electrical conductivity (EC) readings, there was no interaction between irrigation and N-rate treatments on the movement of fertilizer solutes. Total plant N accumulation for SUR and SDI was 12-37% higher than TIME. Plant P accumulation was not affected by either irrigation or N-rate treatments. The nitrogen use efficiency for SUR and SDI was on the order of 37-45%, 56-61%, and 61-68% for 2005, 2006 and 2007, respectively and significantly higher than for the conventional control system (TIME). Moreover, at the intermediate N-rate SUR and SDI systems reduced NO3-N leaching to 5 and 35 kg ha(-1), while at the highest N-rate corresponding values were 7 and 56 kg N ha(-1). Use of N application rates above 220 kg ha(-1) did not result in fruit and/or shoot biomass nor N accumulation benefits, but substantially increased NO3-N leaching for the control treatment, as detected by EC monitoring and by the lysimeters. It is concluded that appropriate use of SDI and/or sensor-based irrigation systems can sustain high yields while reducing irrigation application as well as reducing NO3-N leaching in low water holding capacity soils.
Using sensor data patterns from an automatic milking system to develop predictive variables for classifying clinical mastitis and abnormal milk
Kamphuis, A. ; Pietersma, D. ; Tol, R. van der; Wiedermann, M. ; Hogeveen, H. - \ 2008
Computers and Electronics in Agriculture 62 (2008)2. - ISSN 0168-1699 - p. 169 - 181.
electrical-conductivity - subclinical mastitis - foremilk
Dairy farmers using automatic milking are able to manage mastitis successfully with the help of mastitis attention lists. These attention lists are generated with mastitis detection models that make use of sensor data obtained throughout each quarter milking. The models tend to be limited to using the maximum or average value of the sensor data pattern, potentially excluding other valuable information. They often put cows on the lists unnecessarily, and their sensitivity for abnormal milk classification is too low for automated separation. Therefore, we analyzed sensor data patterns within quarter milkings in order to identify potentially predictive variables for abnormal milk and clinical mastitis classification. The data used in this study was obtained at a commercial dairy farm in Germany in September 2002, where a German Simmental herd was milked by a Lely Astronaut system. In total, 3232 quarter milkings from 63 cows were analysed; 94 quarter milkings were defined as milk with abnormal homogeneity and 270 as clinical mastitis. A data flow diagram was developed to systematically describe the steps involved in the transformation of within quarter milking measurements into variables that potentially predict abnormal milk and clinical mastitis. Three types of pattern descriptors were used: level, variability, and shape. In addition to using the absolute value of the pattern descriptor, the descriptors were considered relative to their expected value based on pattern descriptor values from previous milkings and from other quarters within the same cow milking. Using this method, potentially predictive variables were computed for electrical conductivity, the colours red, green and blue, a combination of colour sensors, and milk production. The importance of a variable in predicting abnormal milk and clinical mastitis was evaluated by computing correlation coefficients as well as information gain ratios. The most important variables came from the sensors for electrical conductivity, blue and green. Variables describing the variability and shape of the measurement patterns were as important as mean and maximum values, and should be included in future modelling. Also variables that are based on absolute values should be considered for future modelling. Results suggest that clinical mastitis and abnormal milk classification models may include similar predictive variables, but requirements for these models differ resulting in the need for different models. The schematic approach to developing potentially predictive variables will be helpful when exploring the usefulness of new sensors, researching other approaches to estimate expected values, and studying sensor data patterns in general.
Sensors and management support in high-technology milking
Hogeveen, H. ; Ouweltjes, W. - \ 2003
Journal of Animal Science 81 (2003)suppl. 3. - ISSN 0021-8812 - p. 1 - 1.
dynamic light-scattering - dairy-cows - automatic milking - electrical-conductivity - robotic milking - body-weight - mastitis - systems - estrus - performance
Two directions can be distinguished in the development of high-tech milking equipment: 1) high-capacity milking parlors with a high throughput of cows per person per hour and 2) automatic milking systems in which manual labor is replaced by a milking robot. High-capacity milking parlors are developed in such a way that one operator is able to milk many cows, partly by automation and partly by optimization of available labor. In such parlors, one operator can milk up to 125 cows per hour. This means that there are only a few seconds available for udder preparation. In an automatic milking system, a robot takes over all manual labor during milking. Currently available systems have one robot arm working with one milking stall (one-stall system) or one robot arm working with more milking stalls (multiple-stall systems). Cows have to go to the automatic milking system voluntarily. Therefore, there is a large variation in milking intervals. Moreover, a large variation between milkings and between cows was observed in milk flow rate, machine-on time and udder preparation time. Both developments in high-tech milking have effects on the milk ejection. The small amount of time dedicated to udder preparation in high-capacity milking parlors has negative effects on the milk ejection, among others leading to more bimodal milk flow curves and longer machine-on time. In automatic milking systems, the variation in time between udder preparation and cluster attachment and in milking frequency might have an effect on milk ejection. Lactation physiology can play a role in solving the questions around milk ejection in high-tech milking systems. The introduction of high-tech milking systems makes decision support systems using sensors necessary. These systems should assist in detection of abnormal milk and mastitis. To a lesser extent, diseased cows need to be brought to the attention of the dairy farmer. Some sensors are currently available for this purpose, but they do not fulfill all demands. In the near future other sensors might be developed. It is important that this development is demand driven and not technology driven. Lactation physiology can play an important role in the determination of milk components useful for automatic detection.
Assessment and field-scale mapping of soil quality properties of a saline-sodic soil
Corwin, D.L. ; Kaffka, S.R. ; Hopmans, J.W. ; Mori, Y. ; Groenigen, J.W. van; Kessel, C. van; Lesch, S.M. ; Oster, J.S. - \ 2003
Geoderma 114 (2003)3-4. - ISSN 0016-7061 - p. 231 - 259.
bodemzoutgehalte - drainage - hergebruik van water - vs - soil salinity - water reuse - usa - spatial prediction methods - san-joaquin valley - electrical-conductivity - shallow groundwater - molybdenum - irrigation - management - selenium - california - waters
Salt-affected soils could produce useful forages when irrigated with saline drainage water. To assess the productive potential and sustainability of using drainage water for forage production, a saline-sodic site (32.4 ha) in California's San Joaquin Valley was characterized for soil quality. The objectives were (1) to spatially characterize initial soil physicochemical properties relevant to maintaining soil quality on an arid zone soil and (2) to characterize soil quality relationships and spatial variability.An initial mobile electromagnetic (EM) induction survey was conducted in 1999, with bulk soil electrical conductivity (ECa) readings taken at 384 geo-referenced locations, followed by an intensive mobile fixed-array survey with a total of 7288 geo-referenced ECa readings. Using the EM data and a spatial statistics program (ESAP v2.0), 40 sites were selected that reflected the spatial heterogeneity of the ECa measurements for the study area. At these sites, soil-core samples were taken at 0.3-m intervals to a depth of 1.2 m. Duplicate samples were taken at eight sites to study the local-scale variability of soil properties. Soil-core samples were analyzed for a variety of physical and chemical properties related to the soil quality of arid zone soils.Soils were found to be highly spatially heterogeneous. For composite soil-core samples taken to a depth of 1.2 m, ECe (electrical conductivity of the saturation extract) varied from 12.8 to 36.6 dS m-1, SAR from 28.8 to 88.8, and clay content from 2.5% to 48.3%. B and Mo concentrations varied from 11.5 to 32.2 mg l-1 and 476.8 to 1959.6 g l-1, respectively. CaCO3, NO3- in the saturation extract, exchangeable Ca2 , Se, and As consistently had the highest coefficients of variation (CV) while pHe, b, and Ca2 in the saturation extract consistently had the lowest CVs at all depths. A one-way analysis of variance (ANOVA) was used to spatially partition the local- and global-scale variability. Local-scale variability was greatest for pHe. Laboratory measurements of saturated hydraulic conductivity (Ks) were very low (0.0000846-0.0456 cm h-1), whereas field measurements were considerably higher (0.49-1.79 cm h-1). Based on the Cl- data, the leaching fraction (LF) for the entire study area was estimated to be 17%.Soil quality was reflected in yield and chemical analysis of forage. Forage Mo contents determined from newly established Bermuda grass varied from 1 to 5 mg kg-1 on a dry matter basis, and Cu/Mo ratios averaged 3.3, while forage yield in the establishment year declined with ECe, and failed to grow above ECe levels of approximately 22 dS m-1. The initial soil quality assessment of the research site indicated that the sustainability of drainage water reuse at this location would depend upon maintaining a sufficient LF with careful consideration and management of salinity, boron, molybdenum, and sodium levels.