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Lai, R., Arca, P., Lagomarsino, A., Cappai, C., Seddaiu, G., Demurtas, C. E., et al. (2017). Manure fertilization increases soil respiration and creates a negative carbon budget in a Mediterranean maize (Zea mays L.)-based cropping system. Catena, 151, 202–212.
Abstract: Agronomic research is important to identify suitable options for improving soil carbon (C) sequestration and reducing soil CO2 emissions. Therefore, the objectives of this study were i) to analyse the on-farm effects of different nitrogen fertilization sources on soil respiration, ii) to explore the effect of fertilization on soil respiration sensitivity to soil temperature (T) and iii) to assess the effect of the different fertilization regimes on the soil C balance. We hypothesized that i) the soil CO2 emission dynamics in Mediterranean irrigated cropping systems were mainly affected by fertilization management and T and ii) fertilization affected the soil C budget via different C inputs and CO2 efflux. Four fertilization systems (farmyard manure, cattle slurry, cattle slurry + mineral, and mineral) were compared in a double-crop rotation based on silage maize (Zea mays L) and a mixture of Italian ryegrass (Lolium multiflorum Lam.) and oats (Avena sativa L). The research was performed in the dairy district of Arborea, in the coastal zone of Sardinia (Italy), from May 2011 to May 2012. The soil was a Psammentic Palexeralfs with a sandy texture (940 g sand kg(-1)). The soil total respiration (SR), heterotrophic respiration (Rh), T and soil water content (SWC) were simultaneously measured in situ. The soil C balance was computed considering the Rh C losses and the soil C inputs from fertilizer and crop residues. The results showed that the maximum soil CO2 emission rates soon after the application of organic fertilizer reached values up to 121,1111 1 111(-2) s(-1). On average, the manure fertilizer showed significantly higher CO2 emissions, which resulted in a negative annual C balance (-2.9 t ha(-1)). T also affected the soil respiration temporal dynamics during the summer, consistently with results obtained in other temperate climatic regions that are characterized by wet summers and contrary to results from rainfed Mediterranean systems where the summer SR and Rh are constrained by the low SWC. The sensitivity of soil respiration to temperature significantly increased with C input from fertilizer. In conclusion, this research supported the hypotheses tested. Furthermore, the results indicated that i) soil CO2 efflux was significantly affected by fertilization management and T, and ii) fertilization with manure increased the soil respiration and resulted in a significantly negative soil C budget. This latter finding could be primarily explained by a reduction in productivity and, consequently, in crop residue with organic fertilization alone as compared to mineral, by the favourable SWC and T for mineralization, and by the sandy soil texture, which hindered the formation of macroaggregates and hence soil C stabilization, making fertilizer organic inputs highly susceptible to mineralization. (C) 2016 Elsevier B.V. All rights reserved.
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Hamidov, A., Helming, K., Bellocchi, G., Bojar, W., Dalgaard, T., Ghaley, B. B., et al. (2018). Impacts of climate change adaptation options on soil functions: A review of European case-studies. Land Degradation & Development, 29(8), 2378–2389.
Abstract: Soils are vital for supporting food security and other ecosystem services. Climate change can affect soil functions both directly and indirectly. Direct effects include temperature, precipitation, and moisture regime changes. Indirect effects include those that are induced by adaptations such as irrigation, crop rotation changes, and tillage practices. Although extensive knowledge is available on the direct effects, an understanding of the indirect effects of agricultural adaptation options is less complete. A review of 20 agricultural adaptation case-studies across Europe was conducted to assess implications to soil threats and soil functions and the link to the Sustainable Development Goals (SDGs). The major findings are as follows: (a) adaptation options reflect local conditions; (b) reduced soil erosion threats and increased soil organic carbon are expected, although compaction may increase in some areas; (c) most adaptation options are anticipated to improve the soil functions of food and biomass production, soil organic carbon storage, and storing, filtering, transforming, and recycling capacities, whereas possible implications for soil biodiversity are largely unknown; and (d) the linkage between soil functions and the SDGs implies improvements to SDG 2 (achieving food security and promoting sustainable agriculture) and SDG 13 (taking action on climate change), whereas the relationship to SDG 15 (using terrestrial ecosystems sustainably) is largely unknown. The conclusion is drawn that agricultural adaptation options, even when focused on increasing yields, have the potential to outweigh the negative direct effects of climate change on soil degradation in many European regions.
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Kipling, R. P., Topp, C. F. E., Bannink, A., Bartley, D. J., Blanco-Penedo, I., Cortignani, R., et al. (2019). To what extent is climate change adaptation a novel challenge for agricultural modellers. Env. Model. Softw., 120, Unsp 104492.
Abstract: Modelling is key to adapting agriculture to climate change (CC), facilitating evaluation of the impacts and efficacy of adaptation measures, and the design of optimal strategies. Although there are many challenges to modelling agricultural CC adaptation, it is unclear whether these are novel or, whether adaptation merely adds new motivations to old challenges. Here, qualitative analysis of modellers’ views revealed three categories of challenge: Content, Use, and Capacity. Triangulation of findings with reviews of agricultural modelling and Climate Change Risk Assessment was then used to highlight challenges specific to modelling adaptation. These were refined through literature review, focussing attention on how the progressive nature of CC affects the role and impact of modelling. Specific challenges identified were: Scope of adaptations modelled, Information on future adaptation, Collaboration to tackle novel challenges, Optimisation under progressive change with thresholds, and Responsibility given the sensitivity of future outcomes to initial choices under progressive change.
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Sándor, R., Barcza, Z., Acutis, M., Doro, L., Hidy, D., Köchy, M., et al. (2016). Multi-model simulation of soil temperature, soil water content and biomass in Euro-Mediterranean grasslands: Uncertainties and ensemble performance. European Journal of Agronomy, .
Abstract: • We simulate biomass, soil water content (SWC) and temperature (ST) in grasslands. • We compare nine models to the multi-model median (MMM) at nine sites. • With model calibration, we obtain satisfactory estimates of ST, less of SWC and biomass. • We observe discrepancies across models in the simulation of grassland processes. • We improve performance with multi-model approach. This study presents results from a major grassland model intercomparison exercise, and highlights the main challenges faced in the implementation of a multi-model ensemble prediction system in grasslands. Nine, independently developed simulation models linking climate, soil, vegetation and management to grassland biogeochemical cycles and production were compared in a simulation of soil water content (SWC) and soil temperature (ST) in the topsoil, and of biomass production. The results were assessed against SWC and ST data from five observational grassland sites representing a range of conditions – Grillenburg in Germany, Laqueuille in France with both extensive and intensive management, Monte Bondone in Italy and Oensingen in Switzerland – and against yield measurements from the same sites and other experimental grassland sites in Europe and Israel. We present a comparison of model estimates from individual models to the multi-model ensemble (represented by multi-model median: MMM). With calibration (seven out of nine models), the performances were acceptable for weekly-aggregated ST (R² > 0.7 with individual models and >0.8–0.9 with MMM), but less satisfactory with SWC (R² < 0.6 with individual models and < ∼ 0.5 with MMM) and biomass (R² < ∼0.3 with both individual models and MMM). With individual models, maximum biases of about −5 °C for ST, −0.3 m3 m−3 for SWC and 360 g DM m−2 for yield, as well as negative modelling efficiencies and some high relative root mean square errors indicate low model performance, especially for biomass. We also found substantial discrepancies across different models, indicating considerable uncertainties regarding the simulation of grassland processes. The multi-model approach allowed for improved performance, but further progress is strongly needed in the way models represent processes in managed grassland systems.
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