Long-term trends in yield variance of temperate managed grassland

Abstract

The management of climate-resilient grassland systems is important for stable livestock fodder production. In the face of climate change, maintaining productivity while minimizing yield variance of grassland systems is increasingly challenging. To achieve climate-resilient and stable productivity of grasslands, a better understanding of the climatic drivers of long-term trends in yield variance and its dependence on agronomic inputs is required. Based on the Park Grass Experiment at Rothamsted (UK), we report for the first time the long-term trends in yield variance of grassland (1965-2018) in plots given different fertilizer and lime applications, with contrasting productivity and plant species diversity. We implemented a statistical model that allowed yield variance to be determined independently of yield level. Environmental abiotic covariates were included in a novel criss-cross regression approach to determine climatic drivers of yield variance and its dependence on agronomic management. Our findings highlight that sufficient liming and moderate fertilization can reduce yield variance while maintaining productivity and limiting loss of plant species diversity. Plots receiving the highest rate of nitrogen fertilizer or farmyard manure had the highest yield but were also more responsive to environmental variability and had less plant species diversity. We identified the days of water stress from March to October and temperature from July to August as the two main climatic drivers, explaining approximately one-third of the observed yield variance. These drivers helped explain consistent unimodal trends in yield variance-with a peak in approximately 1995, after which variance declined. Here, for the first time, we provide a novel statistical framework and a unique long-term dataset for understanding the trends in yield variance of managed grassland. The application of the criss-cross regression approach in other long-term agro-ecological trials could help identify climatic drivers of production risk and to derive agronomic strategies for improving the climate resilience of cropping systems.

Supplementary information: The online version contains supplementary material available at 10.1007/s13593-023-00885-w.

Keywords: Agronomic management; Biomass production; Climate resilience; Fertilizer input; Food security; Liming; Plant species diversity; Soil pH; Temperature; Water stress.

Fig. 1

Park Grass Experiment aerial view (left) and plot layout (right). Location: Harpenden, UK, Herts, AL5 2JQ (51°48′12.33″N; 0°22′21.66″W; 130 m a.s.l.). Detailed information about plot layout and treatments are shown in Tables A1 and A3 Supplementary material. Source: electronic Rothamsted Archive (http://www.era.rothamsted.ac.uk/Park#images/).

Fig. 2

Accumulated number of water stress days for the vegetation period from March to October and temporal development of the mean air temperature (°C) for the months July–August at Rothamsted (1965–2018). Water stress was defined as a limited plant available soil water content (formula described in “Material and methods” section). Further information about the temperature anomaly is provided in Fig. A2 Supplementary material.

Fig. 3

Summary plot for the overview temporal trend in mean yield (blue dotted line; grey crosses real harvest data) and yield variance (red bars) including standard errors (grey error bars) based on the total mean over all liming × fertilization treatments (1965–2018). Underlying plot specific results are provided in Fig. 4; overview of liming treatments (Fig. A8 Supplementary material), and overview of fertilization treatments (Fig. A9 Supplementary material). Detailed information about treatments is shown in Table A3 Supplementary material.

Fig. 4

Temporal trends in mean yield (blue splines with approximated confidence intervals) and temporal yield variance (red bars incl. standard errors) depending on the treatment (fertilizer × liming) for the experimental period of 1965–2018. Analysis based on year × plot specific yields; these raw yield data are shown as black dots in each graphic. A Treatment no. 3: Nil (no fertilizer input)—pH 7/6/5/no chalk. B Treatment no. 7/2: P K Na Mg—pH 7/6/5/no chalk. C Treatment no. 6: N1 P K Na Mg—pH 7/6 (restricted data availability: only 1972–2018 and pH 7/6). D Treatment no. 9/2: N2 P K Na Mg—pH 7/6/5/no chalk. E Treatment no. 11/1: N3 P K Na Mg—pH 7/6/5/no chalk. F Treatment no. 17: N*1 (N* = sodium nitrate)—pH 7/6/5/no chalk. G Treatment no. 13/2: FYM/PM (farmyard/poultry manure)—pH 7/6/5/no chalk. Yield variance denoted Römer’s environmental variance, with lower values indicating more stable yields and higher values indicating more variable yields. Detailed information about treatments is shown in Table A3 Supplementary material. Summary plots are provided as an overall overview (Fig. 3), overview of liming treatments (Fig. A8 Supplementary material), and overview of fertilization treatments (Fig. A9 Supplementary material).

Fig. 5

Graphical visualization of the treatment-specific criss-cross regression analyses (see Table 2; detailed explanation provided in Table A7 Supplementary material) depending on liming. a Treatments with pH 7. b Treatments with pH 6. c Treatments with pH 5. d Treatments with no chalk. The calculation of the environmental mean considered the three selected climatic drivers: the ‘accumulated number of water stress days’ and ‘mean air temperature from May–June and July–August’ (Fig. 2 and Table 1) were used to obtain predicted mean yields for each year (=environmental mean, x-axis) and regressed on the fertilization × liming treatment yields (y-axis); getting regression lines with treatment-specific intercepts and slopes. Treatment explanations are provided in Table A3 Supplementary material (FYM/PM = farmyard/poultry manure; N* = sodium nitrate).