S U M M A R YThe present structure of plant breeding and seed multiplication in India is highly centralized. Furthermore, only a small number of new varieties is officially released each year. The system therefore appears inappropriate for the requirements of the large proportion of Indian farmers located in risk-prone and highly diverse environments. An alternative strategy is described whose central feature is close matching of the characteristics of farmers' traditional rice varieties with those of advanced breeders' lines. A selection from these lines is then distributed in small quantities for on-farm trials managed by farmers themselves. If the success of these initial efforts is to be sustained, a more decentralized approach to breeding and multiplication will be necessary.D. M. Maurya, A. Bottrall y J. Farrington: Sustentos mejorados, diversidad genetica y participation del agricultor: Una estrategia para la selection de arroz en zonas de secano de la India. R E S U M E NLa estructura actual de la fitotecnica y multiplicacion de semillas en la India esta sumamente centralizada. Es mas, cada ano se lanzan oficialmente solo un numero reducido de variedades nuevas. El sistema parece por lo tanto ser inapropiado para los requisitos de la gran proportion de agricultores hindues situados en entornos propensos al riesgo y muy diversos. Se describe una estrategia alternativa, cuya caracteri'stica central es la combination estrecha de las caracteri'sticas de las variedades tradicionales de arroz del agricultor con las de las li'neas avanzadas de los fitotecnicos. Se distribuye una selection de estas li'neas en peguenas cantidades para ensayos en la granja manejados por los mismos agricultores. Si el exito de estos esfuerzos iniciales ha de continuar, hara falta un enfoque mas decentralizado de la fitotecnica y multiplicacion.

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Identification of autoregressive models with exogenous input (ARX) is a classical problem in system identification. This article considers the errors-in-variables (EIV) ARX model identification problem, where input measurements are also corrupted with noise. The recently proposed DIPCA technique solves the EIV identification problem but is only applicable to white measurement errors. We propose a novel identification algorithm based on a modified Dynamic Iterative Principal Components Analysis (DIPCA) approach for identifying the EIV-ARX model for single-input, single-output (SISO) systems where the output measurements are corrupted with coloured noise consistent with the ARX model. Most of the existing methods assume important parameters like input-output orders, delay, or noise-variances to be known. This work's novelty lies in the joint estimation of error variances, process order, delay, and model parameters. The central idea used to obtain all these parameters in a theoretically rigorous manner is based on transforming the lagged measurements using the appropriate error covariance matrix, which is obtained using estimated error variances and model parameters. Simulation studies on two systems are presented to demonstrate the efficacy of the proposed algorithm.

Garlic (Allium sativam L.) is the second important bulb crop after onion. It is very hardy vegetable crop and is grown throughout India. It reduces the cholesterol in the blood. The garlic extracts also the nematicidal fungicidal and bacterial properties. Garlic is in flavorings food, preparing chutneys, pickles, curry powder, tomato ketchup etc. It is rich in proteins, phosphorus, calcium, magnesium and carbohydrates. China rank 1 st in area and production (7.79 lakh ha and 179.68 lakh MT, respectively) and India is the second in area (2.05 lakh ha) and production (10.70 lakh MT). Egypt tops in list (23.83t/ha) productivity followed by China (23.06t/h). The research was conducted during rabi season 2016-2017 at the field of Horticulture department, School of Agricultural Sciences, Career Point University-Kota, Rajasthan, India. The annual rainfall of the region is 650 -1000 mm, most of which is contributed by south west monsoon from July to September. The following observations on various characters were recorded during the period of experimentation is under Shoot observations and Root observations. In experiments indicates that maximum plant height at 30, 60 and 90 days after sowing was found with the treatment T 8 (75% RDF+25% Vermi-compost) and followed by T 5 (50% RDF+50% Vermicompost) and T 1 (control) respectively, while minimum plant height was recorded under the control treatment. It is clearly indicates that maximum number of leaves per plant at 30, 60 and 90 days after sowing were found with the treatment T 8 (75% RDF+ 25% Vermicompost) 4.80,4.61,6.11 per plant followed by T 5 (50% RDF 50% Vermicompost) 3.75, 4.86, 6.34 and T 1 (control) respectfully while minimum number of leaves per plant was recorded under the control treatment. The maximum clove length are recorded under treatment T 8 (75% RDF +25 % Vermi-compost) 2.66 cm and followed by T 5 (50% RDF + 50% Vermicompost) 2.59 cm. While minimum in treatment T 1 (control) 1.67 cm. Length of clove was measure after harvesting. T 8 Treatment and T 5 Treatment also show possible significant response for length of cloves. Length of clove show positive response of good yield.

Dynamic model identification from time series data is a critical component of process control, monitoring, and diagnosis. An important adjunct of model identification is the derivation of filtered estimates of the variables and consequent one-step-ahead prediction errors (residuals) which are very useful for model assessment and iterative model identification. In this work, we present an optimal filtering and residual generation method for the errors-in-variables (EIV) scenario, wherein both the input and output variable measurements are contaminated with errors. The main idea is to combine an EIV-identification strategy with the EIV-Kalman filter (EIV-KF) that is known to provide optimal filtered estimates and residuals of both inputs and outputs for a linear dynamical process in the EIV case. In this work, we combine the EIV-KF with the dynamic iterative principal component analysis (DIPCA) approach that has been recently developed for EIV model identification. This work assumes prominence in that the optimally generated residuals are critical to the tasks of model assessment, fault detection, and diagnosis. The use of residuals in model assessment and reidentification is illustrated in this article, while pointing out that the use of DIPCA alone leads to nonunique filtered estimates and hence nonunique residuals. We remark that the proposed method can be used with any other EIV identification technique.