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Kumar M.,Indian Central Research Institute for Dryland Agriculture | Raghuwanshi N.S.,Indian Institute of Technology Kharagpur | Singh R.,Indian Institute of Technology Kharagpur
Irrigation Science

The use of artificial neural networks (ANNs) in estimation of evapotranspiration has received enormous interest in the present decade. Several methodologies have been reported in the literature to realize the ANN modeling of evapotranspiration process. The present review discusses these methodologies including ANN architecture development, selection of training algorithm, and performance criteria. The paper also discusses the future research needs in ANN modeling of evapotranspiration to establish this methodology as an alternative to the existing methods of evapotranspiration estimation. © 2010 Springer-Verlag. Source

Rakshit S.,Directorate of Sorghum Research | Rakshit A.,Indian Central Research Institute for Dryland Agriculture | Patil J.V.,Directorate of Sorghum Research
Journal of Genetics

Most traits of interest to medical, agricultural and animal scientists show continuous variation and complex mode of inheritance. DNA-based markers are being deployed to analyse such complex traits, that are known as quantitative trait loci (QTL). In conventional QTL analysis, F 2, backcross populations, recombinant inbred lines, backcross inbred lines and double haploids from biparental crosses are commonly used. Introgression lines and near isogenic lines are also being used for QTL analysis. However, such populations have major limitations like predominantly relying on the recombination events taking place in the F 1 generation and mapping of only the allelic pairs present in the two parents. The second generation mapping resources like association mapping, nested association mapping and multiparent intercross populations potentially address the major limitations of available mapping resources. The potential of multiparent intercross populations in gene mapping has been discussed here. In such populations both linkage and association analysis can be conductted without encountering the limitations of structured populations. In such populations, larger genetic variation in the germplasm is accessed and various allelic and cytoplasmic interactions are assessed. For all practical purposes, across crop species, use of eight founders and a fixed population of 1000 individuals are most appropriate. Limitations with multiparent intercross populations are that they require longer time and more resource to be generated and they are likely to show extensive segregation for developmental traits, limiting their use in the analysis of complex traits. However, multiparent intercross population resources are likely to bring a paradigm shift towards QTL analysis in plant species. © 2012 Indian Academy of Sciences. Source

Vijaya Kumar P.,Indian Central Research Institute for Dryland Agriculture
European Journal of Plant Pathology

Weather based prediction models for leaf rust were developed using disease severity and weather data recorded at four locations viz. Ludhiana, Kanpur, Faizabad and Sabour of the All India Wheat and Barley Improvement Project. Weeks 7–9 of the crop growing season at Ludhiana, Faizabad and Sabour and weeks 10–12 at Kanpur were identified as critical periods for relating weather variables to disease. Highly significant correlation coefficients were found between disease severity and a greater number of weather variables in these critical 3-week periods than at other times. The correlation coefficients were greatest for the Humid Thermal Ratio (HTR), Maximum Temperature (MXT) and Special Humid Thermal Ratio (SHTR), and these three weather variables were selected as predictor variables. Linear regressions with these predictor variables (individually) during the critical periods, and a multiple regression with MXT and relative humidity (RH), serve as four disease prediction models, with sufficient lead-time to take control measures. Validation of these prediction models with independent disease severity data showed that the regression equation with MXT (Model-1) was the best among the prediction models, with four out of six simulations matching observed disease severity classes and also having lowest residual sum of squares (SSE) value of 2727. Models 4 (multiple regression), 2 (HTR) and 3 (SHTR) with SSE values of 2881, 3092 and 3732, respectively are in order of decreasing accuracy of prediction. The model using MXT can be used to predict the disease severity in the Indo-Gangetic Plains and provide the basis for efficient disease control. © 2014, Koninklijke Nederlandse Planteziektenkundige Vereniging. Source

Chavan S.B.,Central Agroforestry Research Institute | Rao G.R.,Indian Central Research Institute for Dryland Agriculture | Keerthika A.,Indian Central Arid Zone Research Institute
Indian Journal of Ecology

Studies on measuring CO2, CH4, and N2O fluxes from five agroforestry systems viz., teak, jatropha, pongamia, simaruba and leucaena were conducted at CRIDA, Hyderabad during June-August, 2013 in semi-arid alfisols. The fluxes were measured at weekly interval using closed static chamber technique and gas chromatography method. The highest mean soil CO2 emission observed in jatropha (5644.11 kg ha-1yr-1). teak (4422.90 kg ha-1yr-1) and simamba (4673.58 kg ha-1yr-1), whereas, lower values were recorded in pongamia (4575.28 kg ha-1yr-1) followed by leucaena (2556.94 kg ha-1yr-1). Observations regarding mean uptake in methane showed that in jatropha (8.57 kg ha-1yr-1) and Simaruba (7.37 kg ha-1yr-1) recorded higher values than pongamia (4.02 kg ha-1yr-1) and Teak (3.40 kg ha-1yr-1). The leucaena system (3.50 kg ha-1yr-1) was net emitter of methane as compared with other systems. Highest N2O fluxes during measurement period were observed in simaruba (140.62 kg ha-1yr-1), leucaena (123.96 kg ha-1yr-1) and pongamia (76.26 kg ha-1yr-1). In present study, temperature was most limiting factors than soil moisture among ail the agroforestry systems and produced better fit polynomial models with fluxes of gases. This study gives an idea of successive potential values of GHGs in agroforestry systems to compare with carbon sequestration abilities of these systems. Source

Venkateswarlu B.,Indian Central Research Institute for Dryland Agriculture | Prasad J.V.N.S.,Indian Central Research Institute for Dryland Agriculture
Current Science

Carrying capacity (CC) in the context of Indian agriculture, denotes the number of people and livestock an area can support on a sustainable basis. CC is dynamic in nature, varying from time to time based on utilization of resources, technology application and management. In India, rainfed agriculture occupies nearly 58% of the cultivated area, contributes 40% of country's food production, and supports 40% of the human and 60% of the livestock population. The food grains production has increased several fold in the last four decades. During the last decade (TE 1998-99 to TE 2008-09) the production in coarse cereals, oilseeds and pulses increased by 20%, 16% and 3% respectively, primarily due to the yield gains. There is a need to further increase food production substantially for meeting the requirements of the ever-increasing population. This will put tremendous strain on natural resources which are already under stress due to unsustainable utilization. Continuous decline in groundwater levels, growing deficiency of major and micronutrients, declining factor productivity and looming threat of climate change are some of the issues which will have a bearing on food production in the near future. However, the large realizable yield gaps in many rainfed crops, opportunities to increase yields through rainwater harvesting and recycling, soil fertility improvement, crop diversification and effective dissemination of technologies give a hope that future requirements of food can be met, but it requires substantial resources. This article discusses issues constraining rainfed crop production and possible ways to enhance productivity in a sustainable manner. Source

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