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Grassland productivity estimates informed by soil moisture measurements: Statistical and mechanistic approaches
Agronomy Journal ( IF 2.1 ) Pub Date : 2021-05-01 , DOI: 10.1002/agj2.20709
Erik S. Krueger 1 , Tyson E. Ochsner 1 , Matthew R. Levi 2 , Jeffrey B. Basara 3 , Grant J. Snitker 4 , Briana M. Wyatt 5
Affiliation  

Soil moisture is a fundamental determinant of plant growth, but soil moisture measurements are rarely assimilated into grassland productivity models, in part because methods of incorporating such data into statistical and mechanistic yield models have not been adequately investigated. Therefore, our objectives were to (a) quantify statistical relationships between in situ soil moisture measurements and biomass yield on grasslands in Oklahoma and (b) develop a simple, mechanistic biomass-yield model for grasslands capable of assimilating in situ soil moisture data. Soil moisture measurements (as fraction of available water capacity, FAW) explained 60% of the variability in county-level wild hay yield reported by the National Agricultural Statistics Service (NASS). We next evaluated the performance of mechanistic, evapotranspiration (ET)-driven grassland productivity models with and without assimilation of measured FAW into the models’ water balance routines. Models were calibrated by comparing estimated ET with ET measured using eddy covariance, and calibration proved essential for accurate ET estimates. Models were validated by comparing NASS county-level hay yields to the modeled yields, which were the product of normalized transpiration estimates (the ratio of transpiration to reference ET) and an empirically derived grassland water productivity (the ratio of accumulated biomass to normalized transpiration) estimate. The mechanistic model produced more accurate estimates of wild-hay yields with soil moisture data assimilation (Nash–Sutcliffe efficiency [NSE] = 0.55) than without (NSE = 0.10). These results suggest that improved estimates of grassland productivity could be achieved using in situ soil moisture, which could benefit grazing management decisions, wildfire preparedness, and disaster assistance programs.

中文翻译:

由土壤湿度测量提供的草地生产力估计:统计和机械方法

土壤水分是植物生长的基本决定因素,但土壤水分测量很少被纳入草地生产力模型,部分原因是尚未充分研究将此类数据纳入统计和机械产量模型的方法。因此,我们的目标是 (a) 量化俄克拉荷马州草原原位土壤水分测量与生物量产量之间的统计关系,以及 (b) 为能够同化原位土壤水分数据的草原开发一个简单、机械的生物量产量模型。土壤水分测量值(作为可用水容量的一部分,FAW)解释了国家农业统计局 (NASS) 报告的县级野生干草产量变化的 60%。我们接下来评估了机械的性能,蒸散 (ET) 驱动的草地生产力模型,将测量的 FAW 同化或不同化到模型的水平衡例程中。通过比较估计的 ET 与使用涡流协方差测量的 ET 来校准模型,并且校准证明对于准确的 ET 估计是必不可少的。通过将 NASS 县级干草产量与模拟产量进行比较来验证模型,模拟产量是标准化蒸腾估计值(蒸腾量与参考 ET 的比率)和经验得出的草地水生产力(累积生物量与标准化蒸腾量的比率)的乘积估计。与没有 (NSE = 0.10) 的土壤水分数据同化(Nash-Sutcliffe 效率 [NSE] = 0.55)相比,机械模型对野生干草产量产生了更准确的估计。
更新日期:2021-05-01
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