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

Genetic diversity of morphological, biochemical and mineral traits in Indian onion (Allium cepa) genotypes

The present study was carried out during 2021 and 2022 at ICAR-Indian Agricultural Research Institute, New Delhi to evaluate the diversity among 83 onion (Allium cepa L.) genotypes utilizing morphological, biochemical, and mineral profiling. Substantial genetic variances were observed across all the investigated traits. Traits such as bulb phenol content, bulb pyruvic acid content, neck thickness, average bulb weight, iron, zinc, and sulphur recorded high genotypic coefficient of variance (GCV) and phenotypic coefficient of variance (PCV) values, whereas plant height, total soluble solids, marketable yield, dry matter, and calcium had moderate GCV as well as PCV values. High heritability was observed for all traits except for iron content, which ranged from 98.32% (bulb phenol content) to 37.93% (Iron). Principle Component Analysis (PCA) extracted 5 principal components (PC1–PC5), accounting for a cumulative variance of 59.88%. The primary contributors to PC1 were average bulb weight, marketable yield, and equatorial diameter, while PC2 was primarily influenced by iron content, bulb pyruvic acid content, and neck thickness. On the basis of Euclidean distance and Ward’s D2 analysis, all the genotypes were grouped into three clusters. Cluster 1 showed the highest values for dry matter, iron and zinc content. Cluster 2 consisted of genotypes with higher values for plant height, polar diameter, average bulb weight, calcium, potassium, and sulphur content, whereas it showed lower values for neck thickness. Cluster 3 exhibited higher values for equatorial diameter, total soluble solids and marketable yield. Greater genetic diversity offers breeders enhanced opportunities to identify promising genotypes for selection or utilization as parents in hybrid breeding programmes

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

Weather based fuzzy regression models for prediction of rice yield

Fuzzy regression models for forecasting rice yield in Kanpur district were developed and compared with the weather indices-based regression model. For this, weekly (23-35 SMW) weather data (1971, 1973-2011) were utilized. Significant variables in fuzzy approach were selected based on index of confidence (IC) and adequacy of models was compared with the weather indices-based regression models. It was found that variables such as total accumulation of minimum temperature, weighted interaction of bright sunshine hours and rainfall, weighted interaction of minimum and maximum temperature, unweighted interaction of maximum temperature and relative humidity in morning and weighted interaction of relative humidity in morning and evening respectively, are significant based on their IC and SSE (sum of square error) values. The validations of models were also attempted for three years (2008-09, 2010-11 and 2011-2012).This study also reveals that the parameters for adequacy of models for linear regression models vis-a-vis their fuzzy counterparts are much higher for all values of fitness criterion (h). Thus, fuzzy regression methodology is more efficient than linear regression technique.

Epidemiological models based on meteorological variables to forewarn Alternaria blight of rapeseed-mustard

Alternaria blight [Alternaria brassicae (Berk.) Sacc.] is one of the most widespread and harmful maladies of rapeseed-mustard, causing yield loss up to 47 per cent. Meteorological parameters especially temperature, relative humidity and bright sunshine hours play major role in the development of Alternaria blight disease. Infection by the pathogen is highly influenced by meteorological conditions. A well-tested model based on meteorological variables is an efficient tool for forewarning this disease. Epidemiology of Alternaria blight of brassicas was investigated based on long term data during 2003-2018 crop seasons on the disease severity and meteorological variables, which was validated with data for two subsequent years. During this study, meteorological variable-based regression model of forewarning was developed for maximum severity (%) of Alternaria blight on leaves and pods for three locations viz., New Delhi, Hisar (Haryana) and Mohanpur (West Bengal)] in India. Validation of the forewarning models for maximum severity (%) of Alternaria blight proved the efficiency of the targeted forecasts

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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