Prognosticating crop yield still remains as one of the challenging tasks in agriculture. Even though multiple linear regression methodology has dominated the area of predictive modelling, it is constrained to the assumption that the underlying relationship is assumed to be crisp or precise. Consequently, it often fails to provide satisfactory results when this assumption is violated in realistic situations. Fuzzy linear regression methodology is one of the promising and potential techniques to overcome this lacuna. Moreover, this fuzzy methodology can efficiently handle the problem of multicollinearity. In this paper, an attempt has been made to comparatively assess the efficiency of conventional regression models with their fuzzy counterparts using data on sweet corn yield (t/ha), total weed dry matter (g/m 2 ) at 30 DAS and total weed density (no./m 2 ) at 30 DAS. Model efficiency is computed in terms of average width of the prediction intervals. Efficiency of the models is also assessed in the presence of correlated explanatory variables. Outcomes emanated from the study clearly show the higher relative efficiency of fuzzy linear regression technique in comparison with the widely used simple and multiple linear regression techniques. This study also reveals that the fuzzy methodology has clear advantages over the conventional regression methodology in dealing with correlated explanatory variables.

Weed seed bank in soil as affected by different weed management practices in spring sweet corn

No abstract

In spite of the immense popularity and sheer power of the neural network models, their application in sericulture is still very much limited. With this backdrop, this study evaluates the suitability of neural network models in comparison with the linear regression models in predicting silk cocoon production of the selected six districts (Kolar, Chikballapur, Ramanagara, Chamarajanagar, Mandya and Mysuru) of Karnataka by utilising weather variables for ten consecutive years (2009-2018). As the weather variables are found to be correlated, principal components are obtained and fed into the linear (principal component regression) and non-linear models (back propagation-artificial neural network and extreme learning machine) as inputs. Outcomes emanated from this experiment have revealed the clear advantages of employing extreme learning machines (ELMs) for weather-based modelling of silk cocoon production. Application of ELM would be particularly useful, when the relation between production and its attributing characters is complex and non-linear.

Background: Yield of pigeon pea, besides other management practices, highly relies on variety and pollinators. Again, different insecticides are also known to immensely influence the abundance of these pollinators. Hence, the current study was conducted with objectives to record diversity of flower visitors and abundance of insect pollinators, to work out pollination efficiency of major pollinators and to evaluate the effect of different varieties and insecticidal treatments on the pollinator behaviour, yield and yield attributes of pigeon pea in order to reduce the ecological impact of chemicals as well as to increase the productivity of pigeon pea. Methods: The experiment was designed in a Split plot arrangement replicated thrice. The varieties were assigned in main plots and had four levels such as UPAS-120, PA-406, PA-421 and PA-441. Types of insecticides applied were taken in the sub plots and it also had four levels viz. Quinalphos 25 EC (325 g a.i./ha), Lufenuron 5.4 EC (30 g a.i./ha), Deltamethrin 2.8 EC (12.5 g a.i./ha) and Indoxacarb 15.8 EC (50 g a.i./ha). Result: Outcomes emanated from the study revealed that Megachile disjuncta, Xylocopa latipes and Megachile bicolor were the three most important pollinator bee species on kharif pigeon pea in the tarai region of Uttarakhand. Among the varieties, UPAS-120 and PA-441 had better cross pollination potential over the PA-406 and PA-421. Insecticides had overall negative impact on the insect pollinators. However, Deltamethrin 2.8 EC at recommended dose was found to have least impact on the insect pollinators. Quinalphos 25 EC and Lufenuron 5.4 EC were observed to be more deleterious on pollinators. The ultimate impact on the pollinator insects were profound on the yield and yield attributes of pigeon pea.