  In developing countries like India agriculture plays an extremely important
role in the lives of the population. In India, around 80\% of the population
depend on agriculture or its by-products as the primary means for employment.
Given large population dependency on agriculture, it becomes extremely
important for the government to estimate market factors in advance and prepare
for any deviation from those estimates. Commodity arrivals to market is an
extremely important factor which is captured at district level throughout the
country. Historical data and short-term prediction of important variables such
as arrivals, prices, crop quality etc. for commodities are used by the
government to take proactive steps and decide various policy measures.
  In this paper, we present a framework to work with short timeseries in
conjunction with remote sensing data to predict future commodity arrivals. We
deal with extremely high dimensional data which exceed the observation sizes by
multiple orders of magnitude. We use cascaded layers of dimensionality
reduction techniques combined with regularized regression models for
prediction. We present results to predict arrivals to major markets and state
wide prices for `Tur' (red gram) crop in Karnataka, India. Our model
consistently beats popular ML techniques on many instances. Our model is
scalable, time efficient and can be generalized to many other crops and
regions. We draw multiple insights from the regression parameters, some of
which are important aspects to consider when predicting more complex quantities
such as prices in the future. We also combine the insights to generate
important recommendations for different government organizations.
