  The recent surge in foundation models for natural language processing and
computer vision has fueled innovation across various domains. Inspired by this
progress, we explore the potential of foundation models for time-series
forecasting in smart agriculture, a field often plagued by limited data
availability. Specifically, this work presents a novel application of
$\texttt{TimeGPT}$, a state-of-the-art (SOTA) time-series foundation model, to
predict soil water potential ($\psi_\mathrm{soil}$), a key indicator of field
water status that is typically used for irrigation advice. Traditionally, this
task relies on a wide array of input variables. We explore
$\psi_\mathrm{soil}$'s ability to forecast $\psi_\mathrm{soil}$ in: ($i$) a
zero-shot setting, ($ii$) a fine-tuned setting relying solely on historic
$\psi_\mathrm{soil}$ measurements, and ($iii$) a fine-tuned setting where we
also add exogenous variables to the model. We compare $\texttt{TimeGPT}$'s
performance to established SOTA baseline models for forecasting
$\psi_\mathrm{soil}$. Our results demonstrate that $\texttt{TimeGPT}$ achieves
competitive forecasting accuracy using only historical $\psi_\mathrm{soil}$
data, highlighting its remarkable potential for agricultural applications. This
research paves the way for foundation time-series models for sustainable
development in agriculture by enabling forecasting tasks that were
traditionally reliant on extensive data collection and domain expertise.
