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Determining the Causes of Spatial and Temporal Variability of Wheat Yields at Sub-field Scale Using a New Method of Upscaling a Crop Model

机译:使用扩大化作物模型的新方法确定亚田尺度小麦产量时空变异的原因

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Wheat yields in the Mediterranean climate of Western and Southern Australia are often limited by water. Our measurements on a 70 ha growers field showed linear relationships between grain yield and the plant available soil water storage capacity (PAWc) of the top 100 cm of the soil profile. PAWc was linearly related to apparent soil electrical conductivity measured by proximal sensing using electromagnetic induction (EM38). The APSIM wheat model also employs PAWc as one of the systems parameters and simulated linear relationships between PAWc and yield. These relationships were used to transform an EM38-derived PAWc map of the field into yield maps for three major season types (dry, medium and wet) and nitrogen (N) fertiliser management scenarios. The results indicated that the main cause of temporal and spatial yield variability within the field was due to interactions of seasonal rainfall, PAWc and N fertiliser applications. Spatial variability was low in low rainfall years when yields across the field were low and the higher soil water storage capacity sites were often underutilised. With adequate N, spatial variability increased with seasonal rainfall as sites with higher PAWc conserved more water in wet seasons to give higher yield response than sites with low PAWc. The higher yield response of high PAWc sites to rainfall gave rise to larger temporal variability compared with sites with low PAWc. Provision of adequate N is required for the water limited yield potential to be expressed and this increased both spatial and temporal variability. Sites with low PAWc performed poorly irrespective of rainfall and N application. PAWc is inherently low on deep coarse sands; these sites should be considered for a change in land use. Elsewhere, strategic management interventions should aim to improve PAWc through sub-soil amelioration and deep root growth to increase the capital asset of the farm. The resulting increase in yields will occur in favourable seasons and with adequate fertiliser provisions. The largest grain yield response to water and N will be obtained on sites with the highest PAWc and it is at those sites that the greatest profits from fertiliser use could be achieved in wet seasons.
机译:西澳大利亚州和南澳大利亚州的地中海气候下的小麦单产通常受到水的限制。我们在一个70公顷的种植者田地上进行的测量表明,谷物产量与土壤剖面最顶部100厘米处的植物可用土壤水分存储能力(PAWc)之间存在线性关系。 PAWc与通过电磁感应(EM38)进行近端传感测得的表观土壤电导率线性相关。 APSIM小麦模型还采用PAWc作为系统参数之一,并模拟了PAWc与产量之间的线性关系。这些关系用于将来自EM38的田间PAWc图转换为三种主要季节类型(干,中,湿)和氮肥管理方案的产量图。结果表明,田间时空产量变化的主要原因是季节性降雨,PAWc和氮肥交互作用的结果。在低降雨年份,当田间单产较低且较高的土壤蓄水能力站点经常未被充分利用时,空间变异性较低。当氮素充足时,空间变异性随季节降雨而增加,因为PAWc较高的地区比雨季的PAWc较低的地区可保存更多的水,从而产生更高的产量响应。与低PAWc的地点相比,高PAWc的地点对降雨的更高产量响应导致更大的时间变化。需要提供足够的氮才能表达受水限制的单产潜力,这增加了空间和时间的可变性。无论降水量和施氮量如何,PAWc较低的地区表现都较差。在深层粗砂中,PAWc本质上较低。这些场地应考虑改变土地用途。在其他地方,战略管理干预措施应旨在通过改善土壤深层根深和增加根的生长来增加农场的资本资产,从而改善PAWc。在有利的季节和适当的肥料供应下,单产将会增加。在PAWc最高的地区将获得最大的对水和氮的谷物产量响应,正是在这些地区,在雨季才能从肥料使用中获得最大的利润。

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