Speaker
Description
Introduction
Increasing legume production is a crucial goal of agricultural policy in Germany and the EU, aiming to boost regional protein supply, diversify crop rotations, and mitigate climate change by replacing N fertilizers in cropping systems and by providing alternative plant-based proteins for human nutrition. Narrow-leaved lupin (Lupinus angustifolius) shows promise for temperate climate regions. It is traditionally grown in Northeast-Germany but with potential to wider adaption. The anticipated warming trend in the climate is having noteworthy effects on the growth and yield of lupin and hence its suitability in different parts of Germany and other potential growth regions globally.
Process-based agroecosystem models (AEM) assess genotype × environment × management interactions. AEM capture the soil-plant-atmosphere system simulating crop phenological development, growth and yield formation in hourly to daily time steps. The decision support system for agro-technology transfer (DSSAT) is a widely used modelling platform (Jones et al., 2003) that comprises models for more than 40 different crops. Within DSSAT the generic CROPGRO model is available that is parameterized for different legumes, including peanuts, soybean and faba beans. However, there is so far no lupin model available in DSSAT. Hence, this study, we aim to adapt the DSSAT-CROPGRO model for lupin.
Materials and Methods
Beginning with the CROPGRO faba bean model (Boote, et al., 2002), chosen for its similarity to lupin, we gather information comparing lupin and faba bean characteristics from various sources and available in-house datasets. A valuable source of multi-environment phenological and yield data are the post-registration variety trials. The respective authorities of different states conduct and report those trials annually aiming at informing farmers on regionally recommended genotypes (e.g., Jentsch et al., 2017; Zenk et al., 2017). We further use data from an experiment conducted at the JKI station in Berlin, where four shifted sowing dates are tested annually over three years. The experiment comprises additional data on crop growth, i.e., leaf area index and biomass time series over the growing season. For model adaptation, we decided to go for the common narrow-leaved lupin cv. Boruta first, a cultivar released 2001, which is still cultivated up to date. Boruta is characterized by a determined, i.e., terminal, growth habit featuring a rather clear phenology with little overlap of vegetative and generative growth on the same plant. The assembled dataset encompasses a comprehensive collection of nearly 50 site-years of data for cv. Boruta, providing a robust foundation for our model parametrization. We split the data in ~2/3 for model calibration and ~1/3 for model evaluation.
First, we adapt selected model coefficients building on published sources and define specific coefficients, e.g., maximum grain size, based on analysis of our extensive dataset. For further adapting the model we calibrate multiple cultivar and ecotype coefficients, and utilize the time-series estimator tool (TSE) integrated in the DSSAT framework (Röll, et al., 2020). Notably, this advanced tool facilitates the synchronization of the calibration process across various coefficients and time-series data. Here we first start from phenology parameters, including temperature response, then growth parameters, and finally yield parameters to capture the unique growth characteristics and patterns specific to lupin.
Outlook
The developed CROPGRO lupin model assesses management options, sowing dates, and densities across German regions under current and future climates. As a new crop in DSSAT, it enables the assessment of lupin in different crop rotations at various sites. This will allow to thoroughly assessing its potential to substitute synthetic N fertilizer in crop rotations and evaluate its contribution to resource use efficiency. As the DSSAT AEM simulates daily N2O emissions, the model also allows considering direct and indirect N2O emissions according to Tier 3 approach in GHG accounting. This allows a thorough assessment of the climate change mitigation potential of lupin cultivation in Germany.
Future enhancements involve simulating indeterminate varieties and improving resilience to drought and heat stress through targeted experiments.
Literature
Boote, K.J.; et al. (2002) J. Agron., 94(4): 743-756.
Röll, G.; et al. (2020) Agr. J., 112(5), 3891-3912.
Jones, J.W..; et al. (2003) Eur J Agron., 18(3): 235-265.
Jentsch, U.; et al. (2017) Landessortenversuche in Thüringen, Blaue Lupin, Versuchsbericht 2016. Thüringer Landesanstalt für Landwirtschaft, Jena; Themenblatt-Nr.: 23.02
Zenk, A.; et al. (2017) Sommergetreide und Leguminosen 2017, Ergebnisse Landessortenversuche
| Keywords | narrow-leaved lupin; model; garman plant production; DSSAT; legumes |
|---|
