Pastures and Climate Extremes: Impacts of Cool Season Warming and Drought on the Productivity of Key Pasture Species in a Field Experiment

Rapid quantification of biological nitrogen fixation using optical spectroscopy

Biological nitrogen fixation (BNF) provides a globally important input of nitrogen (N); its quantification is critical but technically challenging. Leaf reflectance spectroscopy offers a more rapid approach than traditional techniques to measure plant N concentration ([N]) and isotopes (δ15N). Here we present a novel method for rapidly and inexpensively quantifying BNF using optical spectroscopy. We measured plant [N], δ15N, and the amount of N derived from atmospheric fixation (Ndfa) following the standard traditional methodology using isotope ratio mass spectrometry (IRMS) from tissues grown under controlled conditions and taken from field experiments. Using the same tissues, we predicted the same three parameters using optical spectroscopy. By comparing the optical spectroscopy-derived results with traditional measurements (i.e. IRMS), the amount of Ndfa predicted by optical spectroscopy was highly comparable to IRMS-based quantification, with R2 being 0.90 (slope=0.90) and 0.94 (slope=1.02) (root mean square error for predicting legume δ15N was 0.38 and 0.43) for legumes grown in glasshouse and field, respectively. This novel application of optical spectroscopy facilitates BNF studies because it is rapid, scalable, low cost, and complementary to existing technologies. Moreover, the proposed method successfully captures the dynamic response of BNF to climate changes such as warming and drought.

Belowground carbon allocation, root trait plasticity, and productivity during drought and warming in a pasture grass

Sustaining grassland production in a changing climate requires an understanding of plant adaptation strategies, including trait plasticity under warmer and drier conditions. However, our knowledge to date disproportionately relies on aboveground responses, despite the importance of belowground traits in maintaining aboveground growth, especially in grazed systems. We subjected a perennial pasture grass, Festuca arundinacea, to year-round warming (+3 °C) and cool-season drought (60% rainfall reduction) in a factorial field experiment to test the hypotheses that: (i) drought and warming increase carbon allocation belowground and shift root traits towards greater resource acquisition and (ii) increased belowground carbon reserves support post-drought aboveground recovery. Drought and warming reduced plant production and biomass allocation belowground. Drought increased specific root length and reduced root diameter in warmed plots but increased root starch concentrations under ambient temperature. Higher diameter and soluble sugar concentrations of roots and starch storage in crowns explained aboveground production under climate extremes. However, the lack of association between post-drought aboveground biomass and belowground carbon and nitrogen reserves contrasted with our predictions. These findings demonstrate that root trait plasticity and belowground carbon reserves play a key role in aboveground production during climate stress, helping predict pasture responses and inform management decisions under future climates.

Spatial modelling of agro-ecologically significant grassland species using the INLA-SPDE approach

The use of spatially referenced data in agricultural systems modelling has grown in recent decades, however, the use of spatial modelling techniques in agricultural science is limited. In this paper, we test an effective and efficient technique for spatially modelling and analysing agricultural data using Bayesian hierarchical spatial models (BHSM). These models utilise analytical approximations and numerical integration called Integrated Nested Laplace Approximations (INLA). We critically analyse and compare the performance of the INLA and INLA-SPDE (Integrated Nested Laplace Approximation with Stochastic Partial Differential Equation) approaches against the more commonly used generalised linear model (glm), by modelling binary geostatistical species presence/absence data for several agro-ecologically significant Australian grassland species. The INLA-SPDE approach showed excellent predictive performance (ROCAUC 0.9271-0.9623) for all species. Further, the glm approach not accounting for spatial autocorrelation had inconsistent parameter estimates (switching between significantly positive and negative) when the dataset was subsetted and modelled at different scales. In contrast, the INLA-SPDE approach which accounted for spatial autocorrelation had stable parameter estimates. Using approaches which explicitly account for spatial autocorrelation, such as INLA-SPDE, improves model predictive performance and may provide a significant advantage for researchers by reducing the potential for Type I or false-positive errors in inferences about the significance of predictors.