Use of a coupled soil-root-leaf model to optimise phosphate fertiliser use efficiency in barley
Use of a coupled soil-root-leaf model to optimise phosphate fertiliser use efficiency in barley
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DOI：
10.1007/s11104-016-2883-4
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发表时间：
2016-04
期刊：
影响因子：
4.9
通讯作者：
J. Heppell;S. Payvandi;P. Talboys;K. Zygalakis;D. Langton;R. Sylvester-Bradley;A. Edwards;R. Walker;P. Withers;Davey L. Jones;T. Roose
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文献类型：
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作者：
J. Heppell;S. Payvandi;P. Talboys;K. Zygalakis;D. Langton;R. Sylvester-Bradley;A. Edwards;R. Walker;P. Withers;Davey L. Jones;T. Roose
目的磷(P)是维持作物生长所必需的营养物质，但在农业生态系统中，磷的利用效率往往很低，这促使工业寻找新的方法来可持续地向作物输送磷。我们的目标是将传统的土壤和作物测量与气候驱动的数学模型相结合，以深入了解优化化肥施用的时机和位置。方法整个植物作物模型结合了地上叶模型和现有的空间显式地下根-土模型来估计植物的磷吸收和地上干质量。我们让依赖于磷的光合作用估计碳(C)质量，结合温度来确定根的生长速率。结果与单独的根-土模型相比，添加叶模型对两组大麦田间试验数据的磷吸收获得了更好的估计。此外，与配施化肥相比，分散配施化肥可使植物对磷的吸收增加高达10%。结论通过捕捉关键的植物过程，我们能够准确地模拟作物季节的P和C利用以及水分和P的移动。机械建模和实验数据的强大结合使生理过程得以准确量化，并为特定地点做出有用的农业预测。
AimsPhosphorus (P) is an essential nutrient necessary for maintaining crop growth, however, it’s often used inefficiently within agroecosystems, driving industry to find new ways to deliver P to crops sustainably. We aim to combine traditional soil and crop measurements with climate-driven mathematical models, to give insight into optimising the timing and placement of fertiliser applications.MethodsThe whole plant crop model combines an above-ground leaf model with an existing spatially explicit below-ground root-soil model to estimate plant P uptake and above ground dry mass. We let P-dependent photosynthesis estimate carbon (C) mass, which in conjunction with temperature sets the root-growth-rate.ResultsThe addition of the leaf model achieved a better estimate of two sets of barley field trial data for plant P uptake, compared with just the root-soil model alone. Furthermore, discrete fertiliser placement increases plant P uptake by up to 10 % in comparison to incorporating fertiliser.ConclusionsBy capturing essential plant processes we are able to accurately simulate P and C use and water and P movement during a cropping season. The powerful combination of mechanistic modelling and experimental data allows physiological processes to be quantified accurately and useful agricultural predictions for site specific locations to be made.