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Evaluating warm-season grass production in temperate-region pastures: A simulation approach

机译:评价温带地区牧草的暖季草产量:一种模拟方法

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摘要

The pasture submodel of the Integrated Farm System Model (IFSM) was modified to simulate the biomass production and nutritive dynamics of a warm-season (C_4) grass monoculture. Predictions of yield and nutritive value were calibrated and evaluated with 5 years of field data from grazed switchgrass (Panicum virgatum) pastures in Pennsylvania, USA. Uncertainty analysis of biomass at the beginning of each growth cycle showed that each estimated yield was sensitive to the amount of biomass that started its growth cycle but not to those of previous growth cycles. Sensitivity analysis showed that predicted yield was most sensitive to physiological parameters such as proportion of photosynthate partitioned to shoots, specific leaf area, structural growth per unit carbohydrate, leaf photosynthetic efficiency, and the light extinction coefficient. Sensitivity of yield to maximum rooting depth increased during periods of drought. For 10 of 13 simulated sampling dates, confidence intervals of field-observed and predicted mean yield overlapped, and half of predicted mean annual yields were within +-32% of observed values. The model predicted seasonal crude protein (half of predictions within +-25%), neutral detergent fiber (all within +-12%), and in vitro true digestibility (all within +-16%) more accurately. Refining the model's representation of warm-season grass phenology and calibrating it to simulate other field experiments may improve predictions of seasonal biomass production. The whole-farm model with a warm-season grass component will provide a useful research and teaching tool for evaluating the long-term economic and environmental sustainability of dairy and beef production systems in warm temperate regions. Published by Elsevier Ltd.
机译:修改了综合农场系统模型(IFSM)的牧场子模型,以模拟暖季(C_4)草单耕种的生物量生产和营养动态。对美国宾夕法尼亚州放牧的柳枝switch(Panicum virgatum)草场的5年实地数据进行了校准,并对其产量和营养价值的预测进行了评估。在每个生长周期开始时对生物量的不确定性分析表明,每个估计的产量对开始其生长周期的生物量均敏感,但对之前的生长周期不敏感。敏感性分析表明,预测的产量对生理参数最为敏感,例如分配给枝条的光合产物比例,比叶面积,每单位碳水化合物的结构生长,叶的光合效率和光消光系数。在干旱期间,产量对最大生根深度的敏感性增加。对于13个模拟采样日期中的10个,实地观测的和预测的平均产量的置信区间重叠,并且一半的预测平均年产量在观测值的+ -32%之内。该模型可以更准确地预测季节性粗蛋白(预测值的一半在+ -25%之内),中性洗涤剂纤维(所有值在+ -12%之内)和体外真实消化率(所有值在+ -16%之内)。完善暖季草物候模型的表示形式并进行校准以模拟其他野外实验,可能会改善季节性生物量产量的预测。具有温暖季节草成分的全农场模型将为评估温暖温带地区乳制品和牛肉生产系统的长期经济和环境可持续性提供有用的研究和教学工具。由Elsevier Ltd.发布

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