Quantitative metrics that measure the global economy's equilibrium have
strong and interdependent relationships with the agricultural supply chain and
international trade flows. Sudden shocks in these processes caused by outlier
events such as trade wars, pandemics, or weather can have complex effects on
the global economy. In this paper, we propose a novel framework, namely:
DeepAg, that employs econometrics and measures the effects of outlier events
detection using Deep Learning (DL) to determine relationships between
commonplace financial indices (such as the DowJones), and the production values
of agricultural commodities (such as Cheese and Milk). We employed a DL
technique called Long Short-Term Memory (LSTM) networks successfully to predict
commodity production with high accuracy and also present five popular models
(regression and boosting) as baselines to measure the effects of outlier
events. The results indicate that DeepAg with outliers' considerations (using
Isolation Forests) outperforms baseline models, as well as the same model
without outliers detection. Outlier events make a considerable impact when
predicting commodity production with respect to financial indices. Moreover, we
present the implications of DeepAg on public policy, provide insights for
policymakers and farmers, and for operational decisions in the agricultural
ecosystem. Data are collected, models developed, and the results are recorded
and presented.

本文提出了一个基于挖掘已有的经济指标与农产品生产价值之间关系的深度学习框架 DeepAg，其中引入了一种名为 LSTM 的深度学习技术对农产品产量进行高精度预测，并结合异常检测算法 Isolation Forests，提高了 DeepAg 模型的预测效果，这对公共政策的制定以及农业生态系统的运营决策阐释具有重要的意义。