Simulation of soybean growth and yield in near-optimal growth conditions
Simulation of soybean growth and yield in near-optimal growth conditions
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模拟接近最佳生长条件下的大豆生长和产量
DOI：
10.1016/j.fcr.2010.07.007
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发表时间：
2010
影响因子：
5.8
通讯作者：
J. D. Bruin
中科院分区：
文献类型：
--
作者：
T. Setiyono;K. Cassman;J. Specht;A. Dobermann;A. Weiss;Haishun Yang;S. Conley;A. Robinson;P. Pedersen;J. D. Bruin
SoySim是一个新的大豆（Glycine max， L. Merr）模拟模型，它将现有的模拟光合作用、生物量积累和分配的方法与几个新的组件结合起来：(i)基于花诱导和诱导后过程的开花，（ii）基于logistic展开和衰老函数的叶面积指数，（iii）使用beta函数整合冠层光合作用，以及（iv）基于同化物供应和种子数量的产量模拟。通过对林肯（NE）、米德（NE）、怀廷（IA）和西拉菲特（IN）地区147个立地年栽培-种植日期-植物-植物种群组合的实地研究数据，验证了SoySim对地上干物质（ADM）和种子产量的模拟效果。在四项实地研究中，除种植日期和植物种群外，每项研究都对农艺管理进行了优化，以实现对害虫、养分或其他可控因素限制最小的生长。SoySim只需要两个基因型特异性和两个作物管理特异性输入参数，就能在美国中北部玉米带广泛的播种日期、植物种群和产量（2.5-6.4Mgha - 1）的最佳生长条件下模拟生长和产量，并提供合理的准确性。模拟种子产量RMSE为0.46Mgha−1。SoySim对特定品种的参数输入要求较少，对关键发育阶段的规格要求较少，物候发育、冠层光合作用和种子干物质积累的机制处理使其在研究中具有若干优势，并可作为决策支持工具来评估作物管理方案对有利环境下产量潜力的影响。
SoySim is a new soybean (Glycine max, L. Merr) simulation model that combines existing approaches for the simulation of photosynthesis, biomass accumulation and partitioning with several new components: (i) flowering based on floral induction and post-induction processes, (ii) leaf area index based on logistic expansion and senescence functions, (iii) integration of canopy photosynthesis using a beta function, and (iv) yield simulation based on assimilate supply and seed number. Simulation of above ground dry matter (ADM) and seed yield by SoySim were validated against data from field studies at Lincoln (NE), Mead (NE), Whiting (IA), and West Lafayette (IN) that included 147 site-year-cultivar-planting date-plant-plant population combinations. In each of the four field studies, agronomic management other than planting date and plant population was optimized to achieve growth with minimal limitation from pests, nutrients, or other controllable factors. SoySim requires just two genotype-specific and two crop management-specific input parameters and yet provides reasonable accuracy in simulating growth and yield under optimum growth conditions across a wide range of sowing dates, plant population, and yield (2.5–6.4Mgha−1) in the North-Central U.S. Corn Belt. Simulated seed yield had a RMSE of 0.46Mgha−1. Few cultivar-specific parameter input requirements, lack of requirements for specification of key developmental stages, and mechanistic treatment of phenological development, canopy photosynthesis, and seed dry matter accumulation give several advantages to SoySim for use in research and for use as a decision-support tool to evaluate the impact of crop management options on yield potential in favorable environments.