Search results

The choice of a sales channel for fresh vegetables is an important decision a farmer can make. Typically, the farmers rely on their personal experience in directing the produce to a sales channel. This study examines how sales forecasting of fresh vegetables along multiple channels enables marginal and small-scale farmers to maximize their revenue by proportionately allocating the produce considering their short shelf life.

Machine learning models, namely long short-term memory (LSTM), convolution neural network (CNN) and traditional methods such as autoregressive integrated moving average (ARIMA) and weighted moving average (WMA) are developed and tested for demand forecasting of vegetables through three different channels, namely direct (Jaivasree), regulated (World market) and cooperative (Horticorp).

The results show that machine learning methods (LSTM/CNN) provide better forecasts for regulated (World market) and cooperative (Horticorp) channels, while traditional moving average yields a better result for direct (Jaivasree) channel where the sales volume is less as compared to the remaining two channels.

The price of vegetables is not considered as the government sets the base price for the vegetables.

The existing literature lacks models and approaches to predict the sales of fresh vegetables for marginal and small-scale farmers of developing economies like India. In this research, the authors forecast the sales of commonly used fresh vegetables for small-scale farmers of Kerala in India based on a set of 130 weekly time series data obtained from the Kerala Horticorp.

The ongoing anthropological transformation urges the rethinking of education, underlining the inadequacy of our schools and universities in dealing with hypercomplexity, that is, with the global extension of all political, social, and cultural processes and with their indeterminacy, interdependence, and interconnection. The idea that educational processes are questions of a purely technical/technological nature, solely a problem of skills and know-how, is the “great mistake” of the hypertechnological society, based on the illusion of being able to measure and quantify everything, to eliminate error and unpredictability, and to achieve total control and rationality. It is necessary to rethink education radically because the extraordinary scientific discoveries and the dynamics of the new technologies have completely overturned the complex interaction between biological and cultural evolution, doing away with the borders between the natural and the artificial. Emergence and emergency themselves are structural features of complex systems (living, social, and human systems), rendered hypercomplex through today’s acceleration and virality, regarding not only education and socialization but also the representations and perceptions of all systemic processes. The merging of fields of knowledge and an epistemology of error become essential for the analysis and interpretation of this hypercomplexity and the unpredictability that distinguishes it.