Seasonal forecast characteristics influence the financial success of farming strategies

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Authors: Willem Adolf Landman, Mark Tadross, Peter Johnston, Olivier Crespo, Emma Archer
Cited by: 0
References: 33

Abstract

Whilst skilful seasonal climate forecasts are routinely produced, their ability to support profitable agricultural decision-making remains uncertain, particularly in regions with modest seasonal predictability and high climate variability. We assess the financial implications of using a statistically derived seasonal maize crop-yield forecast for a medium-size commercial farm in South Africa, focusing on how forecast attributes interact with farmer investment strategies. A cumulative profit framework is used to evaluate three stylised strategies representing different levels of risk exposure under forecast uncertainty. The crop-yield forecasts exhibit measurable skill but limited reliability, notably underpredicting the likelihood of ‘normal’ crop-yield seasons – and this reliability bias has important financial consequences. Reinvesting all available capital can be highly profitable when forecasts are mostly accurate, but leads to catastrophic losses when forecasts are given with a high degree of confidence, but are ultimately inaccurate. Conversely, partial reinvestment offers greater resilience to forecast errors, while a fixed annual investment strategy often produces the most stable long-term outcomes. Riskier strategies are most profitable when forecast weaknesses are avoided. These results demonstrate that even highly skilful seasonal yield forecasts can produce adverse financial outcomes if forecast reliability and decision risk are not explicitly considered. Aligning investment strategies with forecast attributes (such as reliability) and risk tolerance, and complementing standard forecast verification with simple financial metrics, is essential for effective agricultural climate services.

Fields of Study & Subjects

Climate change impacts on agriculture Agricultural risk and resilience Climate variability and models

Publication details

Journal / source
Climate Services
Volume
42
Pages
100642–100642
Publication date
29 January 2026
Language
EN