Forecasting of wheat (Triticum aestivum) yield using ordinal logistic regression

In this study, uses of ordinal logistic model based on weather data has been attempted for forecasting wheat (Triticum aestivum L.) yield in Kanpur district of Uttar Pradesh. Weekly weather data (1971-72 to 2009-10) on maximum temperature, minimum temperature, morning relative humidity, evening relative humidity and rainfall for 16 weeks of the crop cultivation along with the yield data of wheat crop have been considered in the study. Crop years were divided into two and three groups based on the detrended yield. Yield forecast models have been developed using probabilities obtained through ordinal logistic regression along with year as regressors for different weeks. Data from 1971-72 to 2006-07 have been utilized for model fitting and subsequent three years (2007-08 to 2009-10) were used for the validation of the model. Evaluation of the performance of the models developed at different weeks has been done by Adj R2, PRESS (Predicted error sums of squares) and number of misclassifications. Evaluation of the forecasts were done by RMSE (Root mean square error) and MAPE (Mean absolute percentage error) of forecast

Forecasting podfly (Melanogromyza obtusa) in late pigeonpea (Cajanus cajan)

Qualitative and quantitative models were developed for damage due to podfly (Melanogromyza obtusa) on late maturing pigeonpea [Cajanus cajan (L.) Millsp] in Kanpur. Historical data from 1987-88 to 2009-10 on per cent pod damage and weekly weather variables were considered for model fitting. Weather based indices were generated which were used as explanatory variables. Models were validated on subsequent periods (2010-11 and 2011-12) data and found to be satisfactory for both qualitative (epidemic/non-epidemic year) and quantitative (extent of damage)forewarning of damage due to podfly in late pigeonpea at Kanpur

Forecasting technological needs and prioritizing factors in agriculture from a plant breeding and genetics domain perspective: A review

Future technologies in the domain of Indian agriculture are expected to be different from what these are now. The subject of Technology Forecasting (TF) can be resorted to identify the needs to fill the gaps in the present technological trends. As a TF exercise, Brainstorming and Questionnaire approaches were employed to envision future technological needs for one of the subdomains of agriculture, i e Plant Breeding and Genetics (PB&G). Information obtained from experts was subjected to linear combination weighted scoring method for prioritizing key factors leading to future technological needs and were analyzed using multi-dimensional scaling for identifying key agricultural dimensions

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Not AvailableIn this study, uses of ordinal logistic model based on weather data has been attempted for forecasting wheat (Triticum aestivum L.) yield in Kanpur district of Uttar Pradesh. Weekly weather data (1971-72 to 2009-10) on maximum temperature, minimum temperature, morning relative humidity, evening relative humidity and rainfall for 16 weeks of the crop cultivation along with the yield data of wheat crop have been considered in the study. Crop years were divided into two and three groups based on the detrended yield. Yield forecast models have been developed using probabilities obtained through ordinal logistic regression along with year as regressors for different weeks. Data from 1971-72 to 2006-07 have been utilized for model fitting and subsequent three years (2007-08 to 2009-10) were used for the validation of the model. Evaluation of the performance of the models developed at different weeks has been done by Adj R2, PRESS (Predicted error sums of squares) and number of misclassifications. Evaluation of the forecasts were done by RMSE (Root mean square error) and MAPE (Mean absolute percentage error) of forecast.Not Availabl

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Effect of climate change on agriculture or more precisely on insect pests and diseases of agricultural crops is multidimensional. Magnitude of this impact could vary with the type of species and their growth patterns. The elevated agricultural production could be off-set partly or by plant pathogens. It is, therefore, important to consider all the biotic components under the changing pattern of climate. Research world over on the effect of climate change on diseases of crops is inadequate. Several diseases have been noted to be showing higher levels of infestation on different field and horticultural crops in India, which have been discussed. The article also looks at different strategies to cope with effects of climate change on diseases of crops with a proposal for Integrated Decision Support System (IDSS) for Crop Protection Services that suggests the operational focus, research priorities and aspects of capacity building, apart from the thrust on climate-resilient technologies

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Not AvailableSclerotinia sclerotiorum (Lib.) de Bary has worldwide distribution and causes diseases in more than 500 host plants. Sclerotinia rot is a menace to cultivation of oilseed Brassica crops worldwide. The epidemiology of Sclerotinia rot (SR) of Indian mustard (Brassica juncea L.) was investigated during 2004-2012 crop seasons, and based on 8 year of disease data. The forecasting models were developed first time in Indian conditions and then validated in 2012-13. The carpogenic infection initiated in 52 standard week (last week of December) and continued during 1 to 3 standard weeks (first three weeks of January). Disease first appeared after closure of the crop canopy when flowering started. During epidemics, the 8 year mean daily maximum and minimum air temperature was 19.4 and 5.1°C, morning and afternoon RH 95 and 62 per cent, bright sunshine hours 4.9 and rainfall was 1.4 mm, all are conditions favourable for disease development. The R2 value of the regression analysis between observed and estimated SR prevalence was 0.98. Disease forecasting could provide the growers with information for well timed application of fungicides to control SR and this would be beneficial economically.Not Availabl

Plant genetics and breeding research: Scientometric profile of selected countries with special reference to India

184-197Analysis of 32,574 papers published by USA, UK, China, India and Brazil in the field of ‘plant genetics and breeding’ research during 2005-2009 indicates that USA produced the highest number of publications followed by China. The impact of research output as seen by the values of different impact indicators is highest for UK. The sub-domains of emphasis of research shifted in 2009 as compared to 2005 for all countries. India contributed about 9 percent to the world publication output. Indian output formed a part of the mainstream science as seen by the pattern of publication and citation of the research output. The total Indian output originated from 1,806 institutions located in different parts of the country. About 41 percent of the total Indian output was concentrated among 23 institutions. Among the institutions, international institutes located in India had the highest impact. The proportion of single authored papers has decreased considerably and the share of internationally co-authored papers has remained almost steady during the period of study

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Not AvailableUnder Online Pest Monitoring and Advisory Services (OPMAS) program, huge information/data on cotton pest along with weather were collected in three intensive cotton growing zones, viz. the North Zone (Punjab, Haryana and Rajasthan), the Central Zone (Maharashtra, Madhya Pradesh and Gujarat), and the Southern Zone (Andhra Pradesh, Telangana, Karnataka and Tamil Nadu), in India. Based on pest monitoring weekly advisory services were issued to extension agencies and farmers for control measures of pests in the cotton crop. Under the project extraction system was developed which was based on three tier architecture, i.e. presentation, application and data tier to reduce the effort for searching a huge set of data for desired information on real time points. In the system, the central value of pest (mean, maximum and minimum) and spread of the pest in terms of variance and standard deviation may be obtained. These results can provide the epidemic status of the pest based on the threshold values which can be utilized to issue advisories to farmers about the pest control. In future the data extracted from this system can be used for pattern development using pest population as a character under study and time variable as an independent/ explanatory variable.Not Availabl

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Not AvailableIn conventional Trend Impact Analysis (TIA), a baseline model based forecast is generated using historical data. Also, a set of future events and their impacts are identified utilizing prior knowledge. Further, these impacts and events are combined with baseline to generate possible future scenarios through simulation. One of the main drawback of this approach is that it cannot deal with unprecedented future technologies or rare events. Further, it cannot answer about expected future, if some specific event occurs at a particular period in future. Intervention analysis has been traditionally used to assess the impact of any unprecedented event occurring at known times on any time series. It consists of a single impact parameter and a slope parameter for a particular event. Hence, a new TIA method has been developed by combining conventional TIA with the intervention model instead of simulation, The traditional interventional model were modified as per the requirement of TIA to incorporate three impact parameters for any number of events. For the unprecedented future event, impact of the event is known while time at which event will occur is not known in advance. A formula for estimating slope parameter has been derived. The proposed TIA approach is capable to handle the influence of any unusual occurrences on the structure of the fitted model while providing forecasts of future values. The data requirements in this proposed new TIA is less as compared to conventional TIA approach. It can also answer about expected future if some particular event occur in particular time. The proposed TIA approach has been empirically illustrated for wheat yield scenario at All-India level. For this, three events each with three degrees of severity have been considered. All possible scenarios were generated from which preferable futures can be chosen.Not Availabl