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首页> 外文期刊>Italian Journal of Agronomy >Simulation of grass sward dry matter yield in Slovenia using the LINGRA-N model
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Simulation of grass sward dry matter yield in Slovenia using the LINGRA-N model

机译:使用LINGRA-N模型模拟斯洛文尼亚草皮干物质产量

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Calibration and validation of the LINGRA-N model were performed using herbage dry matter (DM) yield data from field studies conducted at three locations in Slovenia. Calibration was done by minimizing root mean square error (RMSE) and validation by using RMSE and Willmott's index of agreement (dw). Calibration of LINGRA-N was not successful for the experiment conducted on permanent grassland in Ljubljana in the period 1974–1993 (RMSE% = 14%, dw = 0.37). Better results were obtained for grass monocultures in Jablje (J) and Raki?an (R) in the period 1998–2013, with the best fit for cocksfoot (Dactylis glomerata L.; RMSE% = 12%, dw = 0.84). Fifty-year simulations were performed for cocksfoot (J-DG) and timothy grass (Phleum pratense L.) in Jablje (J-PP) and perennial ryegrass (Lolium perenne L.) in Jablje (J-LP) and Raki?an. Outliers with very low simulated herbage DM yield were detected only in the second half of the study period and were associated with drought and/or high maximum air temperatures. A time series analysis of annual potential yield values showed a statistically significant (p = 0.05) negative trend for J-LP (-24 kg DM ha- 1year-1) and J-PP (-29 kg DM ha-1year-1). A change in the variability of the reduction factor for crop growth due to drought was already noticeable.
机译:LINGRA-N模型的校准和验证使用在斯洛文尼亚三个地点进行的田间研究的牧草干物质(DM)产量数据进行。通过最小化均方根误差(RMSE)进行校准,并使用RMSE和Willmott一致性指数(dw)进行验证。在1974-1993年间在卢布尔雅那的永久草地上进行的实验中,LINGRA-N的校准不成功(RMSE%= 14%,dw = 0.37)。在1998年至2013年期间,Jablje(J)和Raki?an(R)的草类单作养殖取得了较好的结果,最适合赤脚(Dactylis glomerata L.; RMSE%= 12%,dw = 0.84)。对Jablje(J-PP)的鸡脚(J-DG)和timothy grass(Phleum pratense L.)和Jablje(J-LP)和Raki?an的多年生黑麦草(Lolium perenne L.)进行了50年模拟。仅在研究期的下半年才检测到模拟牧草DM产量非常低的异常值,这些异常值与干旱和/或最高最高气温有关。对年潜在产量值的时间序列分析显示,J-LP(-24 kg DM ha-1year-1)和J-PP(-29 kg DM ha-1year-1)具有统计显着(p = 0.05)负趋势。 。干旱引起的作物生长减少因子变异性的变化已经很明显。

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