Mason Norman W H, Palmer David J, Romera Alvaro, Waugh Deanne, Mudge Paul L
Landcare Research Hamilton New Zealand.
DairyNZ Hamilton New Zealand.
Ecol Evol. 2017 May 30;7(13):4907-4918. doi: 10.1002/ece3.3028. eCollection 2017 Jul.
Agricultural production systems face increasing threats from more frequent and extreme weather fluctuations associated with global climate change. While there is mounting evidence that increased plant community diversity can reduce the variability of ecosystem functions (such as primary productivity) in the face of environmental fluctuation, there has been little work testing whether this is true for intensively managed agricultural systems. Using statistical modeling techniques to fit environment-productivity relationships offers an efficient means of leveraging hard-won experimental data to compare the potential variability of different mixtures across a wide range of environmental contexts. We used data from two multiyear field experiments to fit climate-soil-productivity models for two pasture mixtures under intensive grazing-one composed of two drought-sensitive species (standard), and an eight-species mixture including several drought-resistant species (complex). We then used these models to undertake a scoping study estimating the mean and coefficient of variation (CV) of annual productivity for long-term climate data covering all New Zealand on soils with low, medium, or high water-holding capacity. Our results suggest that the complex mixture is likely to have consistently lower CV in productivity, irrespective of soil type or climate regime. Predicted differences in mean annual productivity between mixtures were strongly influenced by soil type and were closely linked to mean annual soil water availability across all soil types. Differences in the CV of productivity were only strongly related to interannual variance in water availability for the lowest water-holding capacity soil. Our results show that there is considerable scope for mixtures including drought-tolerant species to enhance certainty in intensive pastoral systems. This provides justification for investing resources in a large-scale distributed experiment involving many sites under different environmental contexts to confirm these findings.
农业生产系统正面临着越来越多来自与全球气候变化相关的更频繁、更极端天气波动的威胁。虽然越来越多的证据表明，面对环境波动，植物群落多样性的增加可以降低生态系统功能（如初级生产力）的变异性，但很少有研究测试这在集约化管理的农业系统中是否成立。使用统计建模技术来拟合环境与生产力的关系，提供了一种有效的方法来利用来之不易的实验数据，以比较不同混合物在广泛环境背景下的潜在变异性。我们使用了来自两个多年田间试验的数据，为两种集约放牧下的牧场混合物拟合气候 - 土壤 - 生产力模型——一种由两种对干旱敏感的物种组成（标准型），另一种是包括几种抗旱物种的八物种混合物（复合型）。然后，我们使用这些模型进行了一项范围研究，估计了覆盖新西兰所有地区、具有低、中或高持水能力土壤的长期气候数据中年生产力的均值和变异系数（CV）。我们的结果表明，无论土壤类型或气候条件如何，复合型混合物的生产力变异系数可能始终较低。混合物之间预测的年平均生产力差异受土壤类型的强烈影响，并且与所有土壤类型的年平均土壤水分可利用性密切相关。生产力变异系数的差异仅与最低持水能力土壤的水分可利用性年际变化密切相关。我们的结果表明，包含耐旱物种的混合物在集约化放牧系统中具有很大的潜力来提高稳定性。这为在涉及不同环境背景下多个地点的大规模分布式实验中投入资源以证实这些发现提供了依据。
