Recommendation for consideration in the development of Nepal's Irrigation Master Plan. Part 1 - Management of irrigation systems for effective O & M and resource mobilization; Part 2 - Farmer managed irrigation systems. Occasional paper

Irrigation management / Farmer managed irrigation systems / Resource management / Agricultural production / Nepal

Estimation Of Reference Crop Evapotranspiration Using Fuzzy State Models

Daily evapotranspiration (ET) rates are needed for irrigation scheduling. Owing to the difficulty of obtaining accurate field measurements, ET rates are commonly estimated from weather parameters. A few empirical or semi–empirical methods have been developed for assessing daily reference crop ET, which is converted to actual crop ET using crop coefficients. The FAO Penman–Monteith method, which is now accepted as the standard method for the computation of daily reference ET, is sophisticated. It requires several input parameters, some of which have no actual measurements but are estimated from measured weather parameters. In this study, we examined the suitability of fuzzy logic for estimating daily reference ET with simpler and fewer parameters. Two fuzzy evapotranspiration models, using two or three input parameters, were developed and applied to estimate grass ET. Independent weather parameters from sites representing arid and humid climates were used to test the models. The fuzzy estimated ET values were compared with direct ET measurements from grass–covered weighing lysimeters, and with ET estimations obtained using the FAO Penman–Monteith and the Hargreaves–Samani equations. The estimated ET values from a fuzzy model using three input parameters (Syx = 0.54 mm, r2 = 0.90) were found to be comparable to ET values estimated with the FAO Penman–Monteith equation (Syx = 0.50 mm, r2 = 0.91) and were more accurate than those obtained by the Hargreaves–Samani equation (Syx = 0.66 mm, r2 = 0.53). These results show that fuzzy evapotranspiration models with simpler and fewer input parameters can yield accurate estimation of ET

Optimization Of Fuzzy Evapotranspiration Model Through Neural Training With Input–Output Examples

In a previous study, we demonstrated that fuzzy evapotranspiration (ET) models can achieve accurate estimation of daily ET comparable to the FAO Penman–Monteith equation, and showed the advantages of the fuzzy approach over other methods. The estimation accuracy of the fuzzy models, however, depended on the shape of the membership functions and the control rules built by trial–and–error methods. This paper shows how the trial and error drawback is eliminated with the application of a fuzzy–neural system, which combines the advantages of fuzzy logic (FL) and artificial neural networks (ANN). The strategy consisted of fusing the FL and ANN on a conceptual and structural basis. The neural component provided supervised learning capabilities for optimizing the membership functions and extracting fuzzy rules from a set of input–output examples selected to cover the data hyperspace of the sites evaluated. The model input parameters were solar irradiance, relative humidity, wind speed, and air temperature difference. The optimized model was applied to estimate reference ET using independent climatic data from the sites, and the estimates were compared with direct ET measurements from grass–covered lysimeters and estimations with the FAO Penman–Monteith equation. The model–estimated ET vs. lysimeter–measured ET gave a coefficient of determination (r2) value of 0.88 and a standard error of the estimate (Syx) of 0.48 mm d–1. For the same set of independent data, the FAO Penman–Monteith–estimated ET vs. lysimeter–measured ET gave an r2 value of 0.85 and an Syx value of 0.56 mm d–1. These results show that the optimized fuzzy–neural–model is reasonably accurate, and is comparable to the FAO Penman–Monteith equation. This approach can provide an easy and efficient means of tuning fuzzy ET models

LAND APPLICATION OF SWINE WASTE: REGULATION AND PRODUCER PRACTICES IN OKLAHOMA

Livestock Production/Industries,

Linear Self-Motion Cues Support the Spatial Distribution and Stability of Hippocampal Place Cells

The vestibular system provides a crucial component of place-cell and head-direction cell activity [1-7]. Otolith signals are necessary for head-direction signal stability and associated behavior [8, 9], and the head-direction signal's contribution to parahippocampal spatial representations [10-14] suggests that place cells may also require otolithic information. Here, we demonstrate that self-movement information from the otolith organs is necessary for the development of stable place fields within and across sessions. Place cells in otoconia-deficient tilted mice showed reduced spatial coherence and formed place fields that were located closer to environmental boundaries, relative to those of control mice. These differences reveal an important otolithic contribution to place-cell functioning and provide insight into the cognitive deficits associated with otolith dysfunction

Evaluation Of Methods For Estimating Daily Reference Crop Evapotranspiration At A Site In The Humid Southeast United States

Estimated daily reference crop evapotranspiration (ETo) is normally used to determine the water requirement of crops using the crop factor method. Many ETo estimation methods have been developed for different types of climatic data, and the accuracy of these methods varies with climatic conditions. In this study, pair−wise comparisons were made between daily ETo estimated from eight different ETo equations and ETo measured by lysimeter to provide information helpful in selecting an appropriate ETo equation for the Cumberland Plateau located in the humid Southeast United States. Based on the standard error of the estimate (Syx), the relationship between the estimated and measured ETo was the best using the FAO−56 Penman−Monteith equation (coefficient of determination (r2) = 0.91, Syx = 0.31 mm d−1, and a coefficient of efficiency (E) = 0.87), followed by the Penman (1948) equation (r2 = 0.91, Syx = 0.34 mm d−1, and E = 0.88), and Turc’s equation (r2 = 0.90, Syx = 0.36 mm d−1, and E = 0.88). The FAO−24 Penman and Priestly−Taylor methods overestimated ETo, while the Makkink equation underestimated ETo. The results for the Hargreaves−Samani equation showed low correlation with lysimeter ETo data (r2 = 0.51, Syx = 0.68 mm d−1, and E = 0.20), while those for the Kimberly Penman were reasonable (r2 = 0.87, Syx = 0.40 mm d−1, and E = 0.87). These results support the adoption of the FAO−56 Penman−Monteith equation for the climatological conditions occurring in the humid Southeast. However, Turc’s equation may be an attractive alternative to the more complex Penman−Monteith method. The Turc method requires fewer input parameters, i.e., mean air temperature and solar irradiance data only

Investigation Of A Fuzzy-Neural Network Application In Classification Of Soils Using Ground-Penetrating Radar Imagery

Errors associated with visual inspection and interpretations of radargrams often inhibit the intensive surveying of widespread areas using ground-penetrating radar (GPR). To automate the interpretive process, this article presents an application of a fuzzy-neural network (F-NN) classifier for unsupervised clustering and classification of soil profiles using GPR imagery. The classifier clusters and classifies soil profile strips along a traverse based on common pattern similarities that can relate to physical features of the soil (e.g., number of horizons; depth, texture, and structure of the horizons; and relative arrangement of the horizons, etc.). This article illustrates this classification procedure by its application on GPR data, both simulated and actual. Results show that the procedure is able to classify the profile into zones that corresponded with the classifications obtained by visual inspection and interpretation of radar grams. Application of F-NN to a study site in southwest Tennessee gave soil groupings that are in close correspondence with the groupings obtained in a previous study, which used the traditional methods of complete soil morphological, chemical, and physical characterization. At a crossover value of 3.0, the F-NN soil grouping boundary locations fall within a range of ±2.7 m from the soil groupings determined by the traditional methods. These results indicate that F-NN can supply accurate real-time soil profile clustering and classification during field surveys

The adverse childhood experiences questionnaire: Two decades of research on childhood trauma as a primary cause of adult mental illness, addiction, and medical diseases

Objective. In 1998, Felitti and colleagues published the first study of the Adverse Childhood Experiences-Questionnaire (ACE-Q), a 10-item scale used to correlate childhood maltreatment and adverse rearing contexts with adult health outcomes. This paper qualitatively reviews nearly two decades of research utilizing the ACE-Q, highlighting its contribution to our understanding of the causal roots of common, interlinked comorbidities of the brain and body.Methods. An OVID/PubMed search was conducted for English language articles published before 2016, containing the phrase “Adverse Childhood Experiences” in which the ACE-Q was utilized. Source review included a manual search of bibliographies, resulting in 134 articles, including 44 based on the original ACE-Q study population.Results. ACE-Q research has demonstrated that exposures to adverse childhood experiences converge dose-dependently to potently increase the risk for a wide array of causally interlinked mental illnesses, addictions, and multi-organ medical diseases. The intergenerational transmission of this disease burden via disrupted parenting and insecure rearing contexts is apparent throughout this literature. However, the ACE-Q does not tease out genetic or fetal drug exposure components of this transmission.Conclusions. Adverse childhood experiences and rearing may generate a public health burden that could rival or exceed all other root causes. Translating this information to health-care reform will require strengthening brain-behavioral health as core public and preventative health-care missions. Greater integration of mental health and addiction services for parents should be accompanied by more research into brain mechanisms impacted by different forms and interactions between adverse childhood experiences