Neural Network based Predictors for Evaporation Estimation at Jabalpur in Central India

319-328Free water evaporation is an imperative parameter for estimation of crop water requirement, and irrigation scheduling. This study aims to evaluate different techniques to estimate evaporation with weather parameters inputs. Multilayer Perception (MLR), Radial Basis Function (RBF) based neural network, traditional statistical Linear Regression (LR) approach and conventional empirical methods of Linacre and Christianson were used to estimate the evaporation at Jabalpur station situated under Kymore Plateau and Satpura Hills Agro-climatic Zone of Madhya Pradesh in the Central India. The weather parameters considered for estimation of evaporation are temperature, humidity, sunshine hours and wind speed. Results indicate that MLP and RBF based models with input of all selected weather parameters is able to estimate evaporation much precisely than LR and empirical approaches. It was found that higher accuracy may be obtained with multiple weather data input and low accuracy with only temperature input. It was observed that with temperature used as input the performance accuracy reduces in estimating evaporation with the selected models. However, neural network approach seems to produce better results as compared to statistical and empirical approach. The neural network based model RBF found more efficient in estimation of evaporation as compared to MLP. This study suggests that evaporation can be estimated by RBF model of a station, where there is no standard instrument available for its observation

Reference evapotranspiration modeling using radial basis function neural network in different agro-climatic zones of Chhattisgarh

Precise estimation of evapotranspiration (ET) is extremely essential for efficient utilization of available water resources. Among the empirical models, FAO-Penman-Monteith equation (FAO-PM) is considered as standard method to determine reference evapotranspiration (ET ). In developing countries  like India, application of FAO-PM equation for ET estimation has certain limitations due to unavailability of specific data requirements. Several empirical models such as Hargreaves, Turc, Blaney-Criddle etc., are also considered for ET estimation. However, ET estimates obtain with these models are not comparable with benchmark FAO-PM ET . To address this issue, potential of radial basis function neural network  (RBFNN) is investigated to estimate FAO-PM ET . Result obtained with proposed RBFNN models are compared with equivalent multi-layer artificial neural network (MLANN) and empirical approach of Hargreaves, Turc and Blaney-Criddle. Lower RMSE values obtained with RBFNN and MLANN models is an indication of improved performance over empirical models. Similarly, higher R2 and Efficiency Factor obtained with RBFNN and MLANN models also approves the superiority of machine learning techniques over empirical models. Among the two machine learning techniques, RBFNN models performed better as compared to MLANN. In a nut shell, proposed RBFNN models can simulate FAO-PM ET even with limited  meteorological parameters and consistence degree of accuracy level

Localized Severe Drought during 1996 and Its Impact on Crop Production in Raipur District of Central India

In Raipur district, the onset of the monsoon occurred in the 25th standard meteorological week (June 18–24). But after the onset of monsoonal rains, there was a lull in the monsoon for about 2 consecutive weeks. In the 28th week (July 9–15), the district received 77.6 mm of rainfall. This was equal to the normal value for that week. In the following (29th) week, the district received 96.8 mm rainfall, 38.9% more than the normal rainfall for that week. Thus, the rice, soybean, and other crops sown with the onset of the monsoon in the 25th week suffered from acute water shortage during the 26th and 27th weeks (June 25–July 8), and the germination of these crops was affected. Those farmers who had resown their crop received good rainfall during the 28th and 29th weeks (July 9–22). In the 32nd week, there was a total rainfall of 258.4 mm at Labhandi, Raipur, compared to the weekly normal of 77.1 mm. However, out of this, 222.0 mm of rainfall was received in only one day—July 31/August 1, 1996. Because the rice seedlings were very small at that stage, most farmers drained the water out of their fields

Neural Network based Predictors for Evaporation Estimation at Jabalpur in Central India

Free water evaporation is an imperative parameter for estimation of crop water requirement, and irrigation scheduling. This study aims to evaluate different techniques to estimate evaporation with weather parameters inputs. Multilayer Perception (MLR), Radial Basis Function (RBF) based neural network, traditional statistical Linear Regression (LR) approach and conventional empirical methods of Linacre and Christianson were used to estimate the evaporation at Jabalpur station situated under Kymore Plateau and Satpura Hills Agro-climatic Zone of Madhya Pradesh in the Central India. The weather parameters considered for estimation of evaporation are temperature, humidity, sunshine hours and wind speed. Results indicate that MLP and RBF based models with input of all selected weather parameters is able to estimate evaporation much precisely than LR and empirical approaches. It was found that higher accuracy may be obtained with multiple weather data input and low accuracy with only temperature input. It was observed that with temperature used as input the performance accuracy reduces in estimating evaporation with the selected models. However, neural network approach seems to produce better results as compared to statistical and empirical approach. The neural network based model RBF found more efficient in estimation of evaporation as compared to MLP. This study suggests that evaporation can be estimated by RBF model of a station, where there is no standard instrument available for its observation

Effects of Water Stress on Soybean Productivity in Central India

In the Chhattisgarh plains in the agroclimatic region of central India (Figure 1), farms may be characterized by one of the following: unbunded lathyritic soils, bunded rice fields (rainfed), bunded rice fields (irrigated), unbunded black soils, or rice bunds. Under these five farming situations, different crop sequences have been in vogue. New crops and crop sequences are recommended by the Agricultural University from time to time based on experimental results. In the unbunded black soils, farmers usually plant small millets and pigeon pea. However, based on experimental results, the University has recommended soybean followed by chickpea crop sequence under rainfed conditions during monsoon and post-monsoon (winter) seasons, respectively. In the two to three years since that recommendation, the area under soybeans has increased from 3,000 ha to more than 70,000 ha. Experimental results have shown that the evapotranspiration (ET) rate of the soybean crop during peak vegetative and reproductive stages is very high, ranging between 5 mm and 6 mm per day. In view of this, soybeans have been recommended only for heavy soils. Even in black soils with high retention capacity, water stress conditions do occur during dry spells in the monsoon season. After the withdrawal of monsoon rains in September, soybeans sometimes face acute water shortage during the end of reproductive and maturity stages

Galloway-mowat syndrome - unusual form of nephrotic syndrome in adolescent

Galloway–Mowat syndrome (GMS), also acknowledged as Microcephaly-Hiatal hernia nephrotic syndrome, is an uncommon genetic disorder inherited as an autosomal recessive trait usually seen before two years of life. It is an exceptional multisystem genetic disorder with a collection of skeletal, neurological, facial, gastrointestinal, growth, and renal abnormalities. This case report describes GMS in a girl, suffering from developmental delay, stunted growth, and various dysmorphic features, in whom nephrotic syndrome became apparent at adolescent age

Indian Summer Monsoon variability 140–70 thousand years ago based on multi-proxy records from the Bay of Bengal

Understanding the Indian Summer Monsoon (ISM) behaviour during the late Pleistocene has been largely based on the wind-driven upwelling records from the Arabian Sea. However, it remains unclear the extent to which these records can also be used to infer a concomitant signal of monsoon rainfall, or how the two ISM components, rainfall and wind, are linked on millennial timescales. In order to isolate a primary signal of ISM rainfall, we exploit two deep sea sediment cores from the northern Bay of Bengal (Site U1446) and Andaman Sea (Site U1448), both situated proximal to the South Asian continent, and thus ideally situated for capturing ISM rainfall and fluvial runoff. By comparing our multi-proxy ISM rainfall and runoff records with published ISM wind-driven records from the Arabian Sea, we observe pronounced decoupling of the rainfall and wind components of the ISM across Marine Isotope Stage 5/6 (∼140–70 thousand years ago). We reveal that the relative dominance of barometric dynamics (wind) and the thermodynamic (rainfall) components of the monsoon shifts with changes in background climate state. This finding constitutes an important consideration for the interpretation of past monsoon reconstructions. By comparing our new ISM rainfall records with high latitude climate records, we show that moisture export from low-latitudes, via the monsoon, could have preconditioned the high latitudes for ice sheet growth during glacial inceptions

Toxigenic fusarium species and fumonisin b1 and b2 associated with freshly harvested sorghum and maize grains produced in Karnataka, India

Fumonisins are toxic secondary metabolites produced by the species of Fusarium. Accurate detection of these toxins in cereals is essential for a reliable evaluation of human exposure to these carcinogenic mycotoxins. In the present study, maize and sorghum samples were randomly collected from different regions of Karnataka (India) used for the analysis of fumonisin contamination. Preliminary mycological studies of samples confirmed the occurrence of mycotoxigenic Fusarium species such as F. verticillioides (18 strains), F. proliferatum (2 strains) and F. anthophilum (2 strains). The method used for the analysis of fumonisins was solvent extraction, C18 Sep-Pak reverse phase clean-up and orthophthalaldehyde plus 2-mercaptoethanol derivatization followed by high-performance liquid chromatography with fluorescence detector. The FB1 and FB2 concentration in maize patties inoculated with three different Fusarium species ranged between 0.065 to 121.42 µg/g. Fumonisin concentrations in 13 of 15 natural maize samples were found to be 0.003-23.43 µg/g. similarly FB1 and FB2 concentrations in 07 of 13 natural sorghum samples were found to be 0.001 to 17.09 µg/g. The average yield of FB1 produced was much higher concentration to that of FB2 in all the samples analyzed. Further, FB2 was always found in association with FB1 in all the samples analyzed by HPLC. This is first report on the natural occurrence of fumonisins in cereals produced in Karnataka (India)