Assessing on-farm productivity of Miscanthus crops by combining soil mapping, yield modelling and remote sensing
Assessing on-farm productivity of Miscanthus crops by combining soil mapping, yield modelling and remote sensing
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DOI：
10.1016/j.biombioe.2015.12.024
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发表时间：
2016-02
影响因子：
6
通讯作者：
G. Richter;F. Agostini;A. Barker;D. Costomiris;A. Qi
中科院分区：
文献类型：
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作者：
G. Richter;F. Agostini;A. Barker;D. Costomiris;A. Qi
来自农业土地的生物质是任何可持续生物能源战略的关键组成部分，第二代木质纤维素原料是英国政府实现减少二氧化碳排放目标的政策的一部分。收获前对生物质供应潜力的估计通常是以实验证据为基础的，对生物学可获得的产量与实际可获得的农场生物质产量之间的差距知之甚少。我们提出了一个系统的整合映射信息适合估计可获得的产量，使用经验模型，观察到的农场产量和遥感。因此，人们可以确定产量变化和供应不确定性的来源。空间上明确的芒的潜在产量进行了比较，从建立作物种植后≥5年，在能源作物计划的参与者中进行了调查的农场产量。实际农场产量平均为8.94 Mg ha− 1，变化很大（变异系数为34%），很大程度上与土壤类型无关。粘土上的平均产量差距（37%）远大于桑迪和壤土（10%）。芒草是显着较慢，建立在粘土上所示的拟合逻辑Gompertz方程产生的时间序列。然而，通过密度计数、目视检查（谷歌地球）和遥感（陆地卫星5号）确定的作物覆盖差距与观测到的农场产量相关，表明斑块是产量下降的原因。分析显示了改善这些新作物农艺学的方法，以增加供应链内的经济回报和环境效益（减少温室气体排放，增加碳固存），并减少生物能源生产的土地需求。
Biomass from agricultural land is a key component of any sustainable bioenergy strategy, and 2ndgeneration, ligno-cellulosic feedstocks are part of the UK government policy to meet the target of reduced CO2emission. Pre-harvest estimates of the biomass supply potential are usually based on experimental evidence and little is known about the yield gap between biologically obtainable and actual achievable on-farm biomass yields. We propose a systematic integration of mapped information fit for estimating obtainable yields using an empirical model, observed on-farm yields and remote sensing. Thereby, one can identify the sources of yield variation and supply uncertainty. Spatially explicit Miscanthus potential yields are compared with delivered on-farm yields from established crops ≥5 years after planting, surveyed among participants in the Energy Crop Scheme. Actual on-farm yield averaged at 8.94 Mg ha−1and it varied greatly (coefficient of variation 34%), largely irrespective of soil type. The average yield gap on clay soils was much larger than that on sandy or loamy soils (37% vs 10%). Miscanthus is noticeably slower to establish on clay soils as shown by fitting a logistic Gompertz equation to yield time series. However, gaps in crop cover as identified by density counts, visual inspection (Google Earth) and remote sensing (Landsat-5) correlated with observed on-farm yields suggesting patchiness as causal for reduced yields. The analysis shows ways to improve the agronomy for these new crops to increase economic returns within the supply chain and the environmental benefits (reduced GHG emission, greater carbon sequestration) and reduce the land demand of bio-energy production.