  The rapid decline in groundwater around the world poses a significant
challenge to sustainable agriculture. To address this issue, agricultural
managed aquifer recharge (Ag-MAR) is proposed to recharge the aquifer by
artificially flooding agricultural lands using surface water. Ag-MAR requires a
carefully selected flooding schedule to avoid affecting the oxygen absorption
of crop roots. However, current Ag-MAR scheduling does not take into account
complex environmental factors such as weather and soil oxygen, resulting in
crop damage and insufficient recharging amounts. This paper proposes MARLP, the
first end-to-end data-driven control system for Ag-MAR. We first formulate
Ag-MAR as an optimization problem. To that end, we analyze four-year in-field
datasets, which reveal the multi-periodicity feature of the soil oxygen level
trends and the opportunity to use external weather forecasts and flooding
proposals as exogenous clues for soil oxygen prediction. Then, we design a
two-stage forecasting framework. In the first stage, it extracts both the
cross-variate dependency and the periodic patterns from historical data to
conduct preliminary forecasting. In the second stage, it uses weather-soil and
flooding-soil causality to facilitate an accurate prediction of soil oxygen
levels. Finally, we conduct model predictive control (MPC) for Ag-MAR flooding.
To address the challenge of large action spaces, we devise a heuristic planning
module to reduce the number of flooding proposals to enable the search for
optimal solutions. Real-world experiments show that MARLP reduces the oxygen
deficit ratio by 86.8% while improving the recharging amount in unit time by
35.8%, compared with the previous four years.
