# Methods for Mid-Term Forecasting of Crop Export and Production
## 1 Introduction  
The COVID-19 pandemic triggered a drop in all types of production, a decrease in trade flows, and rupture of cross-country communications around the world [1]. On the one hand, those challenges require urgent measures to develop agriculture successfully. On the other hand, commercial food has every chance of becoming the primary export item and an alternative even to energy sources, providing international weight and social stability within many countries. At the same time, the current level of grain production does not correspond to the potential of soil resources, is not an indicator of sustainability, and the recent modern agricultural technologies are not adequately implemented. Therefore, reaching the competitive level of grain yield is the primary goal of agriculture.  
Because the state resources are limited, the efforts should be focused on a small set of prospective development directions. Namely, the development of grain production and export implies discovering a restricted set of the new prospective commodity items, which would be the primary growth drivers. Moreover, that set should be revealed as soon as possible because all state-level measures in agriculture usually require several years to be implemented. This problem can be defined as a mid-term forecast of a particular commodity's export and production level; therefore, one can analyze various export items and leave only the most prospective ones. Thus, such forecast models can be applied as a part of the software to optimize country and regional-level agricultural managementin case of insufficient resources (water regime of soils, plant nutrition, yield forecasts) and achieve better sustainability of agriculture. That problem is not trivial because of the following issues:  
* Variable character of agricultural trade flows.
* Too many features influence trade flows and production. Using them all would lead to over-complex prediction models, which are not trainable with the dataset. Mid-term forecasting requires the use of complex models that consider many features and parameters from past observations, but the size of the training dataset is strictly limited [2]. Therefore, complex models can be easily over-fitted and, in some cases, provide incorrect results on unseen data [3].
* Political decisions and economic sanctions strongly affect trade flows and production, but they can hardly ever be predicted using only statistic databases. Additional information such as news and social media messages should be considered.  
The paper presents neural network models for mid-term forecasting wheat production and export, considering heterogeneous temporal and spatial features such as trade flows, production levels, macroeconomic indicators, fuel pricing, and vegetation indexes. We also use text-mining to detect events in the news flow related to the state of agricultural policy, sanctions, and the context in the local and international agricultural markets. The proposed models have value in practical applications since they provide accurate mid-term forecasting of crop production and export and consider non-structural features, which can significantly affect the markets.  
In this paper, we tackle the problem by answering the following research questions:  
1. Can the textual features improve export and production forecasting?
2. Which neural network architectures can integrate heterogeneous structured and textual features to provide accurate forecasting?
3. Is there any training regularization to increase the forecasting reliability and reduce the overfitting?