Efficient crop model parameter estimation and site characterization using large breeding trial data sets
Efficient crop model parameter estimation and site characterization using large breeding trial data sets
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DOI：
10.1016/j.agsy.2017.07.016
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发表时间：
2017-10-01
影响因子：
6.6
通讯作者：
Arachchige, Pabodha Galgamuwe
中科院分区：
文献类型：
--
作者：
Lamsal, Abhishes;Welch, S. M.;Arachchige, Pabodha Galgamuwe
科学家估计，到 2050 年，全球农作物产量需要增加一倍才能满足食品、饲料和燃料的需求。为了实现这一目标，需要新的方法来通过加强管理策略来提高育种潜在收益率以及农场产量。这两项任务都需要能够根据最终与遗传学相关的品种特征（临界短日照长度、最大叶片光合速率、荚果填充持续时间等）知识来预测植物在多种动态环境中的表现。由于这种联系，我们将此类性状称为基因型特异性参数（GSP）。利用行业提供的 353 个站点年的产量和天气数据，我们估计了 182 个品种中每个品种的 7 个主要 CROPGRO-大豆 GSP。该数据集有两个缺点。首先，没有提供种植日期，导致作物实际经历的环境不可知。其次，仅提供了顶部 20 厘米的土壤数据，这不足以指定根部环境和供水可用性。因此，获得了额外的土壤信息。开发了一种新颖的优化算法，可以同时估计 GSP 和种植日期，同时调整分层土壤持水特性。我们将优化器命名为全息遗传算法 (HGA)，它使用外部提供的约束和自身对数据结构的分析，将超过 2000 个维度的搜索减少到数量少得多的重叠 1 维到 3 维问题。进行了两种类型的运行。第一个之前是对已发布的 GSP 进行独立成分分析 (ICA)。随后的培训寻求良好的成分分数，而不是 GSP 本身。第二种单独因素（SF）方法允许所有普惠制单独变化。这使得参数不受约束并且分布更均匀。结果表明，HGA 与 CROPGRO-Soybean 模型配合得很好，可以根据育种试验数据估计品种和特定地点的参数。两种运行类型的校准和评估质量相似，RMSE 值约为。最大收益率的5.6%。此外，品种的普惠制可用于预测该品种校准中未使用的试验中的产量。最后，尽管维度很高，但所有品系和地点的 GSP、种植日期和土壤特性在 < 58 次迭代中同时收敛，展示了大数据集的巨大实用性。
Scientists have estimated that global crop production needs to double by 2050 to supply the demand for food, feed, and fuel. To reach this goal, novel methods are needed to increase breeding potential yield rates of gain as well as on-farm yields through enhanced management strategies. Both of these tasks require the ability to predict plant performance in multiple, dynamic environments based on a knowledge of cultivar characteristics (critical short day lengths, maximum leaf photosynthetic rates, pod fill durations, etc.) that are ultimately linked to genetics. Because of this linkage, we refer to such traits as genotype-specific parameters (GSP's). Using industry provided yield and weather data from 353 site-years, we estimated seven primary CROPGRO-Soybean GSP's for each of 182 varieties. The data set had two shortcomings. First, no planting dates were supplied, rendering unknowable the environment actually experienced by the crop. Second, soil data were provided only for the top 20 cm, which is inadequate to specify the root environment and water supply availability. Therefore, additional edaphic information was acquired. A novel optimization algorithm was developed that simultaneously estimates GSP's and planting dates, while tuning layered soil water-holding properties. The optimizer, which we have named the holographic genetic algorithm (HGA), uses both externally supplied constraints and its own analysis of data structure to reduce what would otherwise be a search over 2000 dimensions to a much smaller number of overlapping 1- to 3-D problems. Two types of runs were performed. The first was preceded by an independent component analysis (ICA) of published GSP's. The subsequent training sought good component scores rather than the GSP's themselves. The second, separate factor (SF) approach allowed all GSP's to vary separately. This makes parameters unconstrained and more evenly distributed. Results showed that HGA works quite well with the CROPGRO-Soybean model to estimate the cultivar and site-specific parameters from breeding trial data. The quality of the calibrations and evaluations were similar across both run types with RMSE values being ca. 5.6% of the maximum yields. Moreover, the GSP's for a variety can be used to predict its yield in trials not used in that cultivars calibration. Finally, despite high dimensionality, the GSP's, planting dates, and soil properties for all lines and sites converged concurrently in < 58 iterations, demonstrating great utility for use with big data sets.