Modelling the effects of pasture renewal on the carbon balance of grazed pastures

Exploring the diversity and composition of soil microbial communities in different soybean-maize management systems

Soybean-maize are cultivated in different management systems, such as no-tillage and pastures, which presents potential to add organic residues, and it can potentially impacts the soil microbial community present in these systems. Thus, this study aimed to examine the effects of different soybean-maize management practices on the diversity and composition of soil microbial communities. Specifically, 16 S rRNA amplicon sequencing was used to investigate whether the use of pasture species in a fallowing system influences microbial communities in a soybean-maize rotation system, as compared to conventional tillage and no-tillage systems. The results indicate that the inclusion of the pasture species Urochloa brizantha in soybean-maize management systems leads to distinct responses within the soil microbial community. It was found that different soybean-maize management systems, particularly those with U. brizantha, affected the microbial community, likely due to the management applied to this pasture species. The system with 3 years of fallowing before soybean-maize showed the lowest microbial richness (∼2000 operational taxonomic units) and diversity index (∼6.0). Proteobacteria (∼30%), Acidobacteria (∼15%), and Verrucomicrobia (∼10%) were found to be the most abundant phyla in the soil under tropical native vegetation, while soils under cropland had an increased abundance of Firmicutes (∼30% to ∼50%) and Actinobacteria (∼30% to ∼35%). To summarize, this study identified the impacts of various soybean-maize management practices on the soil microbial community and emphasized the advantages of adding U. brizantha as a fallow species.

Could patterns of animal behaviour cause the observed differences in soil carbon between adjacent irrigated and unirrigated pastures?

Previous soil sampling from grazed pastures in New Zealand compared the changes of soil organic carbon (SOC) in adjacent irrigated and unirrigated portions of the same paddocks. It showed that irrigated portions had lower SOC stocks than unirrigated portions, with an average difference of 7.0 tC ha^-1 or 0.6 tC ha^-1 yr^-1. These findings have formed the basis of an assessment for the net effect of conversion of New Zealand's grazed pastures to irrigation. However, since cattle could move freely between irrigated and unirrigated portions of the studied paddocks, there could have been different grazing intensities and/or excreta transfer between the irrigated and unirrigated portions of the same paddocks. Both these factors could have affected SOC stocks. In this study, we used the process-based model, CenW, to simulate the consequences of this possible carbon transfer via animal excreta and different grazing intensities. We found that the observed increase of 0.6 tC ha^-1 yr^-1 in SOC stock in the unirrigated portions could result from a transfer of 20% excreta from the irrigated to unirrigated portions (with an area ratio of 6:1) of a paddock and with the unirrigated portions being grazed only lightly with 2.0 tDM ha^-1 in foliage biomass residuals remaining after grazing. That means that the observed higher SOC stocks in the unirrigated portions could potentially be attributable to the behaviour of grazing animals. We suggest that a realistic extent of carbon transfer and/or differences in grazing intensities could be sufficient to account for the observed differences in SOC stocks even if irrigation per se caused no differences in carbon stocks. It is therefore inappropriate to ascribe the change of SOC to irrigation effects based on experimental findings where SOC changes can be affected by the behaviour of grazing animals.

Combining eddy covariance measurements with process-based modelling to enhance understanding of carbon exchange rates of dairy pastures

Many temperate grasslands are used for dairying, and ongoing research aims to better understand these systems in order to increase animal production and soil organic carbon (SOC) stocks. However, it is difficult to fully understand management effects on SOC because most changes are slow and difficult to distinguish from natural variability, even if changes are important over years to decades. Eddy covariance (EC) measurements can overcome this problem by continuously measuring net carbon exchange from pastures, but net balances are very sensitive to even small systematic measurement errors. Combining EC measurements with detailed process-based modelling can reduce the risks inherent in total reliance on EC measurements. Modelling can also reveal information about the underlying processes that drive observed fluxes. Here, we describe carbon exchange patterns of five paddocks situated at four different locations in New Zealand and France where EC data and detailed physiological modelling were available. The work showed that respiration by grazing animals was often only incompletely captured in EC measurements. This was most problematic when fluxes were based on gap-filling, which could have estimated incorrect fluxes during grazing periods based on observations from periods without grazing. We then aimed to extract plant physiological insights from these studies. We found appreciable carbon uptake rates even at temperatures below 0 °C. After grazing, carbon uptake was reduced for up to 2 weeks. This reduction was larger than expected from reduced leaf area after grazing, but the factors contributing to that difference have not yet been identified. Detailed physiological models can also extrapolate findings to new management regimes, environmental conditions or plant attributes. This overcomes the limitation of experimental studies, which are necessarily restricted to actual site and weather conditions allowing models to make further progress on predicting management effects on SOC.