English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

To achieve good crop yields, farmers are aware of the importance of good rainfall during the growing season. However, their choice of crop to plant may not necessarily be the optimal choice when climate uncertainty exists. Indeed, current cropping allocations may be driven more by historical trends and tradition than by the future forecasts of climate scenarios and commodity prices. In a digital agricultural future, farmers may instead use information on crop yields and water usage to make cropping decisions each year that optimize their cash flow under the uncertainty of climate conditions and commodity prices. In this paper, we apply dynamic portfolio optimization techniques to the development of a simulation-based numerical method for making dynamic optimal cropping decisions. This method relies on a backwards recursive approach developed to solve the American option pricing problem. At each time step backwards from the end of the decision time period, the optimal expected future cash flow, or the so-called continuation function, is determined by using the Least Squares Monte Carlo method. As an example, we use a representative farm in Australia with four paddocks that can grow wheat, rice, barley and canola, and we also regard the corresponding commodity prices as stochastic variables. We compute the optimal crop rotations each year under different rainfall scenarios that maximise the expected utility over a fixed time period of 20 years. We evaluate the performance of the dynamic cropping strategies by comparing the expected value and standard deviation of future cash flows against those generated from static cropping strategies.

Choosing crop rotations under uncertainty: a multi-period dynamic portfolio optimization approach