Diet Prevents Social Stress-Induced Maladaptive Neurobehavioural and Gut Microbiota Changes in a Histamine-Dependent Manner

Exposure to repeated social stress may cause maladaptive emotional reactions that can be reduced by healthy nutritional supplementation. Histaminergic neurotransmission has a central role in orchestrating specific behavioural responses depending on the homeostatic state of a subject, but it remains to be established if it participates in the protective effects against the insults of chronic stress afforded by a healthy diet. By using C57BL/6J male mice that do not synthesize histamine (Hdc^−/−) and their wild type (Hdc^+/+) congeners we evaluated if the histaminergic system participates in the protective action of a diet enriched with polyunsaturated fatty acids and vitamin A on the deleterious effect of chronic stress. Behavioural tests across domains relevant to cognition and anxiety were performed. Hippocampal synaptic plasticity, cytokine expression, hippocampal fatty acids, oxylipins and microbiota composition were also assessed. Chronic stress induced social avoidance, poor recognition memory, affected hippocampal long-term potentiation, changed the microbiota profile, brain cytokines, fatty acid and oxylipins composition of both Hdc^−/− and Hdc^+/+ mice. Dietary enrichment counteracted stress-induced deficits only in Hdc^+/+ mice as histamine deficiency prevented almost all the diet-related beneficial effects. Interpretation: Our results reveal a previously unexplored and novel role for brain histamine as a mediator of many favorable effects of the enriched diet. These data present long-reaching perspectives in the field of nutritional neuropsychopharmacology.     

Assimilation of satellite data into agrohydrological models to improve crop yield forecasts

This paper addresses the question of whether data assimilation of remotely sensed leaf area index and/or relative evapotranspiration estimates can be used to forecast total wheat production as an indicator of agricultural drought. A series of low to moderate resolution MODIS satellite data of the Borkhar district, Isfahan (Iran) was converted into both leaf area index and relative evapotranspiration using a land surface energy algorithm for the year 2005. An agrohydrological model was then implemented in a distributed manner using spatial information of soil types, land use, groundwater and irrigation on a raster basis with a grid size of 250 m, i.e. moderate resolution. A constant gain Kalman filter data assimilation algorithm was used for each data series to correct the internal variables of the distributed model whenever remotely sensed data were available. Predictions for 1 month in advance using simulations with assimilation at a regional scale were very promising with respect to the statistical data (bias = ±10%). However, longer‐term predictions, i.e. 2 months in advance, resulted in a higher bias between the simulated and statistical data. The introduced methodology can be used as a reliable tool for assessing the impacts of droughts in semi‐arid regions.     

Long-Term Effects of Irrigation Water Conservation on Crop Production and Environment in Semiarid Areas

As water is becoming a scarcer commodity, savings in the irrigation sector could enhance water development in areas currently not being irrigated, and arrest the rapid environmental degradation due to waterlogging in arid areas. The agro-hydrological model SWAP is used to investigate possible water reductions for wheat and cotton crops under shallow water table conditions prevailing in the Fourth Drainage Project in Punjab, Pakistan. The simulations are performed for both drained and undrained conditions considering three different irrigation water qualities. The overall objective is to save good-quality irrigation water. The results indicate that when good-quality canal water is available, a reduced application to wheat (195 mm) and cotton (260 mm) will keep the soil healthier under both drained and undrained conditions. For poor-quality irrigation waters (mixed canal and tubewell or tubewell alone), this water conservation strategy will be insufficient. Therefore, more water (325 mm for wheat and 325 mm for cotton) should be applied to keep crop production and soil salinity within desirable limits. However, this will only be applicable to the areas where proper subsurface drainage systems are present. For undrained areas, this strategy will not be feasible due to rising water tables; other options like growing more salt-tolerant crops should be considered. Drainage cannot solve salinity buildup problems under all circumstances because relatively dry monsoons provide insufficient leaching water, and salts added by tubewell irrigation can only be evacuated from the soil profile if the drainage system is very intense. Reduced irrigation inputs is a proper short-term solution, although wheat production tends to decline in all areas without drainage, even when irrigated with canal water. Large-scale drainage investments associated with adjusted irrigation planning seem unavoidable in the long run.     

Integrating spatial statistics and remote sensing

This paper presents an integrated approach towards spatial statistics for remote sensing. Using the layer concept in Geographical Information Systems we treat successively elements of spatial statistics, scale, classification, sampling and decision support. The layer concept allows to combine continuous spatial properties with classified map units. The paper is illustrated with five case studies: one on heavy metals in groundwater at different scales, one on soil variability within seemingly homogeneous units, one on fuzzy classification for a soillandscape model, one on classification with geostatistical procedures and one on thermal images. The integrated approach offers a better understanding and quantification of uncertainties in remote sensing studies.     

Remote Sensing and Economic Indicators for Supporting Water Resources Management Decisions

This paper demonstrates that combining spatial land surface data with socio-economic analysis provides a number of indicators to strengthen decision making in integrated water and environmental management. It provides a basis to: track current water consumption in the Inkomati Basin in South-Africa; adjust irrigation water management; select crop types; facilitate planning; estimate crop yields before harvesting, and consequently to forecast market price development. Remote sensing data and economic analysis can also be used to study the spatial distribution of water consumption as an indicator of equity in access to water resources. It even enables identification of farms that consume more irrigation water than formally allocated. Finally, it provides a basis to assess the cost-effectiveness of various ways to reduce agricultural water consumption. So, this approach is potentially useful for determining water consumption, refining water allocation policies, and determining the potential for water transfers through mechanisms such as water trading.     

Local calibration of remotely sensed rainfall from the TRMM satellite for different periods and spatial scales in the Indus Basin

The availability of accurate rainfall data at proper temporal and spatial scales is vital for knowledge of renewable water resources and safe withdrawals for irrigation. Rain gauge networks in mountainous basins such as the Indus are sparse and insufficient to plan withdrawals and water management applications. Satellite rainfall estimates can be used as an alternative source of information but need area-specific calibration and validation due to the indirect nature of the radiation measurements. In this study, a calibration protocol is worked out for rainfall data from the Tropical Rainfall Measuring Mission (TRMM) satellite because uncalibrated TRMM rainfall data are inaccurate for use in rainfall–runoff studies and in soil water balance studies. Two alternative techniques, regression analysis (RA) and geographical differential analysis (GDA), were used to calibrate TRMM rainfall data for different periods and spatial distributions. The validity of these techniques was tested using Nash–Sutcliffe efficiency and the standard error of estimate. The GDA technique proved to be better, with higher efficiency and smaller error in complex mountainous terrains. The deviation between TRMM data and rain gauge data was decreased considerably from 10.9% (pre-calibration at 625 km²) to 6.1% (post-calibration at 3125 km²) for annual time periods. For monthly periods, the deviation of 34.9% (pre-calibration at 625 km²) was decreased to 15.4% (post-calibration at 3125 km²). Calibration can be improved further if more rain gauges are available. The GDA technique can be applied to calibrate TRMM rainfall data in regions with limited rain gauge data and can provide a sufficiently accurate estimate of the key hydrological process that can be used in water management applications.     

Learning procedure in a neural control model for the urinary bladder

A continuous neural network coupled to a dynamical model of the urinary bladder is defined. The neural network is trained to control the bladder model to track a prescribed volume fluctuation, by adjusting weights and time constants. The gradients of the error in the output neurons of the neural network are unknown. Therefore, the learning procedure discussed here minimizes the error functional without using gradient descent.     

Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Applications

Recent satellite image processing developments have provided the means to calculate evapotranspiration (ET) as a residual of the surface energy balance to produce ET “maps.” These ET maps (i.e., images) provide the means to quantify ET on a field by field basis in terms of both the rate and spatial distribution. The ET images show a progression of ET during the year or growing season as well as its spatial distribution. The mapping evapotranspiration at high resolution with internalized calibration (METRIC) is a satellite-based image-processing procedure for calculating ET. METRIC has been applied with high resolution Landsat images in southern Idaho, southern California, and New Mexico to quantify monthly and seasonal ET for water rights accounting, operation of ground water models, and determination of crop coefficient populations and mean curves for common crops. Comparisons between ET by METRIC, ET measured by lysimeter, and ET predicted using traditional methods have been made on a daily and monthly basis for a variety of crop types and land uses. Error in estimated growing season ET was 4% for irrigated meadow in the Bear River basin of Idaho and 1% for an irrigated sugar beet crop near Kimberly, Id. Standard deviation of error for time periods represented by each satellite image averaged about 13 to 20% in both applications. The results indicate that METRIC and similar methods such as SEBAL hold substantial promise as efficient, accurate, and inexpensive procedures to estimate actual evaporation fluxes from irrigated lands throughout growing seasons.     

Mapping Root Zone Soil Moisture Using Remotely Sensed Optical Imagery

Field-based soil moisture measurements are cumbersome. Remote sensing techniques based on active or passive microwave data have limitations. This paper presents and validates a new method based on land surface energy balances using remotely sensed optical data (including thermal infrared), which allows field and landscape-scale mapping of soil moisture depth-averaged through the root zone of existing vegetation. Root zone depth can be variable when crops are emerging. The pixel-wise “evaporative fraction” (ratio of latent heat flux to net available energy) is related to volumetric soil moisture through a standard regression curve that is independent of soil and vegetation type. Validation with measured root zone soil moisture in cropped soils in Mexico and Pakistan has a root mean square error of 0.05?cm3?cm?3; the error is less than 0.07?cm3?cm?3 in 90% of cases. Consequently, soil moisture data should be presented in class intervals of 0.05?cm3?cm?3. The utility of this method is demonstrated at the field scale using multitemporal thematic mapper imagery for irrigated areas near Cortazar in Mexico, and for river basin-scale water resources distribution in Pakistan. The potential limitation is the presence of clouds and the time lag between consecutive images with field-scale resolution. With the falling price of optical satellite imagery, this technique should gain wider acceptance with river basin planners, watershed managers, and irrigation and drainage engineers.     

Irrigation Performance using Hydrological and Remote Sensing Modeling

Development of water saving measures requires a thorough understanding of the water balance. Irrigation performance and water accounting are useful tools to assess water use and related productivity. Remote sensing and a hydrological model were applied to an irrigation project in western Turkey to estimate the water balance to support water use and productivity analyses. Remote sensing techniques can produce high spatial coverage of important terms in the water balance for large areas, but at the cost of a rather sparse temporal resolution. Hydrological models can produce all the terms of the water balance at a high temporal, but low spatial resolution. Actual evapotranspiration for an irrigated area in western Turkey was calculated using the surface energy balance algorithm for land (SEBAL) remote sensing land algorithm for two Landsat images. The hydrological model soil-water-atmosphere-plant (SWAP) was setup to simulate the water balance for the same area, assuming a certain distribution in soil properties, planting dates and irrigation practices. A comparison between evapotranspiration determined from SEBAL and from SWAP was made and differences were minimized by adapting the distribution in planting date and irrigation practice. The optimized input data for SWAP were used to simulate all terms of the accumulated water balance for the entire irrigation project, and subsequently used to derive the irrigation performance indicators. The innovative methodology presented is attractive as it diminishes the need of field data and combines the strong points of remotely sensed techniques and hydrological models.