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Development and evaluation of HUME-OSR: A dynamic crop growth model for winter oilseed rape
Field Crops Research ( IF 5.6 ) Pub Date : 2020-02-01 , DOI: 10.1016/j.fcr.2019.107679
Ulf Böttcher , Wiebke Weymann , Jeroen W.M. Pullens , Jørgen E. Olesen , Henning Kage

Abstract A new dynamic crop growth model based on empirically derived allometric partitioning rules was developed for winter oilseed rape. The model simulates dry matter production, nitrogen uptake and distribution, leaf, stem and pod area expansion and yield formation under optimal and water- and nitrogen-limited conditions. The model includes hibernation, senescence due to self-shading, freezing and aging, translocation of assimilates and nitrogen, light absorption and reflection by flower layer and oil synthesis. It was parameterized with two data sets from Hohenschulen, northern Germany, and validated with datasets from Germany, France, Great Britain, and the Czech Republic. Model performance in terms of prediction of total aboveground dry matter production gave an RMSE of 180 g m−2 and the linear regression between measured and simulated root/shoot ratios gave an r2 of 0.64. In addition, nitrogen uptake (RMSE 4.26 g m−2) and distribution (r2 simulated/measured leaf N/stem N = 0.44) are quite well represented. In contrast, year-to-year variability of final seed yield was less correctly estimated, probably due to variation in the harvest index, which is not included in the model prediction. Relative differences in seed yield due to sowing date and nitrogen application were well reproduced in most cases. Therefore the model has potential to be used for supporting optimization of management strategies, climate change scenario studies and future breeding progress.

中文翻译:

HUME-OSR 的开发与评价:一种冬季油菜作物动态生长模型

摘要 基于经验得出的异速生长划分规则,为冬季油菜开发了一种新的动态作物生长模型。该模型模拟了最佳和水和氮限制条件下的干物质生产、氮吸收和分配、叶、茎和豆荚面积扩大以及产量形成。该模型包括冬眠、自遮光衰老、冷冻和老化、同化物和氮的易位、花层和油合成的光吸收和反射。它使用来自德国北部 Hohenschulen 的两个数据集进行参数化,并使用来自德国、法国、英国和捷克共和国的数据集进行验证。在预测总地上干物质生产方面的模型性能给出了 180 gm-2 的 RMSE,并且测量和模拟根/芽比之间的线性回归给出了 0.64 的 r2。此外,氮吸收(RMSE 4.26 gm-2)和分布(r2 模拟/测量的叶 N/茎 N = 0.44)得到了很好的体现。相比之下,最终种子产量的逐年变化估计不太准确,可能是由于收获指数的变化,这不包括在模型预测中。在大多数情况下,由于播种日期和施氮而导致的种子产量的相对差异可以很好地重现。因此,该模型具有用于支持优化管理策略、气候变化情景研究和未来育种进展的潜力。氮吸收(RMSE 4.26 gm-2)和分布(r2 模拟/测量的叶 N/茎 N = 0.44)得到了很好的体现。相比之下,最终种子产量的逐年变化估计不太准确,可能是由于收获指数的变化,这不包括在模型预测中。在大多数情况下,由于播种日期和施氮而导致的种子产量的相对差异可以很好地重现。因此,该模型具有用于支持优化管理策略、气候变化情景研究和未来育种进展的潜力。氮吸收(RMSE 4.26 gm-2)和分布(r2 模拟/测量的叶 N/茎 N = 0.44)得到了很好的体现。相比之下,最终种子产量的逐年变化估计不太准确,可能是由于收获指数的变化,这不包括在模型预测中。在大多数情况下,由于播种日期和施氮而导致的种子产量的相对差异可以很好地重现。因此,该模型具有用于支持优化管理策略、气候变化情景研究和未来育种进展的潜力。这不包括在模型预测中。在大多数情况下,由于播种日期和施氮而导致的种子产量的相对差异得到了很好的再现。因此,该模型具有用于支持优化管理策略、气候变化情景研究和未来育种进展的潜力。这不包括在模型预测中。在大多数情况下,由于播种日期和施氮而导致的种子产量的相对差异可以很好地重现。因此,该模型具有用于支持优化管理策略、气候变化情景研究和未来育种进展的潜力。
更新日期:2020-02-01
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