Accounting for soil water improves prediction of lentil phenology for improved frost and heat stress management

Abstract

Lentils in Australia are primarily grown in temperate and Mediterranean climates, especially in the southern and western regions of the country. As in other parts of the world, lentil yields in these areas are significantly influenced by factors such as frost, heat, and drought, contributing to variable production. Therefore, selecting appropriate lentil varieties and determining optimal sowing times that align with favourable growing conditions is crucial. Accurate predictions of crop development are essential in this context. Current models mainly rely on photoperiod and temperature to predict lentil phenology; however, they often neglect the impact of soil water on flowering and pod set. This study investigated whether incorporating soil water as an additional factor could improve predictions for these critical growth stages. The modified model was tested using 281 data points from various lentil experiments that examined the timing of flowering (61–147 days) and pod set (77–163 days) across different combinations of location, variety, sowing time, and season. The results indicated that including soil water in the prediction model achieved an R² value of 0.84 for flowering and 0.83 for pod set. The normalised root mean square error (NRMSE) was 0.07, and Lin's concordance correlation coefficient (LinCCC) was 0.91. The model produced an R² of 0.88, an NRMSE of 0.05, and a LinCCC of 0.93 flowering compared to the default model, which yielded an R² of 0.24, an NRMSE of 0.17, and a LinCCC of 0.36 for flowering. A limited sensitivity analysis of the modified model showed that variations in initial soil water and in-season rainfall significantly affected the timing of flowering and pod set. Additionally, we employed a probability framework to assess the crop's vulnerability to the last frost day and early heat stress events during the reproductive stage. This approach provided valuable insights for decision-making to mitigate risks associated with frost and heat stress. Our study suggests that integrating soil water dynamics into lentil phenology models improves the accuracy and precision of predictions regarding the timing of flowering and pod set. These improvements lead to better forecasts, ultimately helping to minimise damage from frost and heat stress during lentil cultivation and can better explain the effect of climate variability.

Plant phenotyping relevance

土壌水分を組み込んだレンズマメの開花・莢形成時期予測モデルを開発し、複数実験データで性能検証している。予測対象は明示的な植物フェノロジー形質であり、モデル手法が研究の中心である。

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