Speaker
Description
Introduction
While agricultural production is among the main drivers of anthropogenic climate change, projected effects of climate change and climate variability increase the pressure to provide food in sufficient quantity and quality at the same time. Inadequate nitrogen (N)-fertilisation practices, that fail to consider seasonally variable weather conditions and their impacts on crop yield potential and N-requirements, cause reduced N-use efficiency. As a result, both the ecological and economic sustainability of crop production are at risk. Forecasts of crop yield and development have thus been promoted as promising means towards more targeted fertilisation practices. The aim of this study was to develop a season-specific crop forecasting approach that allows for a targeted N-application in winter wheat while maintaining farm revenue compared to empirical N-fertilisation practices. Traditionally, crop forecasts are based on climatological records. Evidence that future growing conditions will deviate from historic averages and the improvement of weather forecasting skill gave reason to move to crop forecasts that use seasonal weather forecasts for this study instead.
Material and Methods
The present crop forecasts were generated using the process-based crop model SSM iCrop (Soltani and Sinclair, 2012) combined with state-of-the-art seasonal ensemble weather forecasts (SEAS5, Johnson et al., 2019) downscaled to a 1 km grid over the case study region of Eastern Austria. Forecasts included key N-management variables, such as phenological stages (PHEN), above-ground dry weight and grain yield (GRNY), total N-uptake (CNUP), as well as plant-available soil water and mineral N-content. Precise predictions of PHEN were required for an accurate timing of N-fertilisation, while GRNY and CNUP forecasts defined the amount of N (N-amount) applied.
Throughout the season, these variables were forecasted through monthly iCrop runs for three winter wheat on-farm experiments in Eastern Austria. iCrop was supplied with observed daily weather data from sowing until the last day of each previous month, and downscaled seasonal forecasts from then until harvest. To quantify the operational potential of using these crop forecasts for in-season N-fertilisation, each experimental field was divided into two treatments: (i) FARM, where N-amount was applied according to common farm practice, and (ii) crop forecast (CROF), where N-amount was applied such that forecasted mean GRNY and protein content (PROT) met FARM levels but forecasted CROF economic return to applied N (ERAN) was increased through a reduction of N-amount compared to FARM.
Each field was treated as a complete block. Significant differences in ERAN were tested through an ANOVA model, including block and treatment as fixed effects.
Results and Discussion
Results from the three on-farm experiments showed a reduction in NAmount of -23.42% (-43.33 kgN ha^-1) when implementing CROF compared to FARM. While maintaining revenue from high-quality grain sales (PROT>14%), lower N-amount led to a significant benefit of +30.22% (+2.20 € kgN^-1) in ERAN (Figure 1, see attached).
Figure 1 caption: Total amount of nitrogen applied (N-amount), revenue, and economic return to applied N (± standard deviation) of winter wheat in Eastern Austria, fertilised according to crop forecasts (CROF) and farm practice (FARM). Lower case letters indicate significant differences between treatments (p<0.05).
Prior to this study, iCrop was extensively parameterised and tested against comprehensive field datasets in the target wheat production region (Manschadi et al., 2022). For applying the present approach and extrapolating the results of this study to other production environments, iCrop (or any other crop model used) first needs to be adapted and parameterised to capture local conditions well. However, under the projected increase in non-average growing conditions, in-season N-application is expected to further benefit from crop forecasts in the future overall.
References
Johnson, S.J. et al., 2019. SEAS5: the new ECMWF seasonal forecast system. Geoscientific Model Development 12, 1087-1117.
Manschadi, A.M. et al., 2022. Performance of the SSM-iCrop model for predicting growth and nitrogen dynamics in winter wheat. European Journal of Agronomy 135.
Soltani, A. and Sinclair, T.R., 2012. Modeling physiology of crop development, growth and yield. Modeling Physiology of Crop Development, Growth and Yield, 1-322.
| Keywords | Crop forecasts; Nitrogen fertilisation; Winter wheat; On-farm experiments; Economic return to applied nitrogen |
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