A restatement of the natural science evidence base concerning grassland management, grazing livestock and soil carbon storage.

Approximately a third of all annual greenhouse gas emissions globally are directly or indirectly associated with the food system, and over a half of these are linked to livestock production. In temperate oceanic regions, such as the UK, most meat and dairy is produced in extensive systems based on pasture. There is much interest in the extent to which such grassland may be able to sequester and store more carbon to partially or completely mitigate other greenhouse gas emissions in the system. However, answering this question is difficult due to context-specificity and a complex and sometimes inconsistent evidence base. This paper describes a project that set out to summarize the natural science evidence base relevant to grassland management, grazing livestock and soil carbon storage potential in as policy-neutral terms as possible. It is based on expert appraisal of a systematically assembled evidence base, followed by a wide stakeholders engagement. A series of evidence statements (in the appendix of this paper) are listed and categorized according to the nature of the underlying information, and an annotated bibliography is provided in the electronic supplementary material.

Modelling climate change impacts on maize yields under low nitrogen input conditions in sub-Saharan Africa.

Smallholder farmers in sub-Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low-input systems is currently lacking. We evaluated the impact of varying [CO2 ], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi-arid Rwanda, hot subhumid Ghana and hot semi-arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in-season soil water content from 2-year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO2 ], temperature and rainfall. Without N fertilizer input, maize (a) benefited less from an increase in atmospheric [CO2 ]; (b) was less affected by higher temperature or decreasing rainfall; and (c) was more affected by increased rainfall because N leaching was more critical. The model intercomparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low-input systems. Climate change and N input interactions have strong implications for the design of robust adaptation approaches across SSA, because the impact of climate change in low input systems will be modified if farmers intensify maize production with balanced nutrient management.

Biogeochemical Research Priorities for Sustainable Biofuel and Bioenergy Feedstock Production in the Americas.

Rapid expansion in biomass production for biofuels and bioenergy in the Americas is increasing demand on the ecosystem resources required to sustain soil and site productivity. We review the current state of knowledge and highlight gaps in research on biogeochemical processes and ecosystem sustainability related to biomass production. Biomass production systems incrementally remove greater quantities of organic matter, which in turn affects soil organic matter and associated carbon and nutrient storage (and hence long-term soil productivity) and off-site impacts. While these consequences have been extensively studied for some crops and sites, the ongoing and impending impacts of biomass removal require management strategies for ensuring that soil properties and functions are sustained for all combinations of crops, soils, sites, climates, and management systems, and that impacts of biomass management (including off-site impacts) are environmentally acceptable. In a changing global environment, knowledge of cumulative impacts will also become increasingly important. Long-term experiments are essential for key crops, soils, and management systems because short-term results do not necessarily reflect long-term impacts, although improved modeling capability may help to predict these impacts. Identification and validation of soil sustainability indicators for both site prescriptions and spatial applications would better inform commercial and policy decisions. In an increasingly inter-related but constrained global context, researchers should engage across inter-disciplinary, inter-agency, and international lines to better ensure the long-term soil productivity across a range of scales, from site to landscape.

Application of the JULES-crop model and agrometeorological indicators for forecasting off-season maize yield in Brazil.

Zea mays L is a crucial crop for Brazil, ranking second in terms of production and sixth in terms of exports. In Brazil, the second season, or off-season, accounts for 80 % of the overall maize output, which primarily occurs after the soybean main season. A maize yield forecast model for the off-season was developed and implemented throughout Brazilian territory due to its importance to the country's economy and food security. The model was built using multiple linear regressions that connected outputs simulated from a land surface model used in large-scale analysis for agriculture (JULES-crop), to agrometeorological indicators. The application of the developed model occurred every 10 days from the sowing until the maturation. A comparison of the forecasting model was verified with the official off-season maize yields for the years 2003-2016. Agrometeorological indicators during the reproductive phase accounted for 60 % of the interannual variability in maize production. When outputs simulated by JULES-crop were included, the forecasting model achieved Nash-Sutcliffe modeling efficiency (EF) of 0.77 in the maturation and EF = 0.72 in the filling-grain stage, suggesting that this approach can generate useful predictions for final maize yield beginning on the 80th day of the cycle. Outputs of JULES crop enhanced modeling performance during the vegetative stage, reducing the standard deviation error in prediction from 0.59 to 0.49 Mg ha-1.

Stakeholder-driven transformative adaptation is needed for climate-smart nutrition security in sub-Saharan Africa.

Improving nutrition security in sub-Saharan Africa under increasing climate risks and population growth requires a strong and contextualized evidence base. Yet, to date, few studies have assessed climate-smart agriculture and nutrition security simultaneously. Here we use an integrated assessment framework (iFEED) to explore stakeholder-driven scenarios of food system transformation towards climate-smart nutrition security in Malawi, South Africa, Tanzania and Zambia. iFEED translates climate-food-emissions modelling into policy-relevant information using model output implication statements. Results show that diversifying agricultural production towards more micronutrient-rich foods is necessary to achieve an adequate population-level nutrient supply by mid-century. Agricultural areas must expand unless unprecedented rapid yield improvements are achieved. While these transformations are challenging to accomplish and often associated with increased greenhouse gas emissions, the alternative for a nutrition-secure future is to rely increasingly on imports, which would outsource emissions and be economically and politically challenging given the large import increases required.

The EAT-Lancet reference diet and cognitive function across the life course.

The EAT-Lancet Commission devised a sustainable reference diet with the aim of reducing the incidence of non-communicable diseases and mortality globally while improving food system sustainability. The extent to which the reference diet supports cognitive function across the life course, however, has not yet been evaluated. This Review assesses the evidence for diet supporting cognitive function from childhood into old age. A comprehensive but non-exhaustive literature search was done, synthesising studies that investigated the effect of whole foods on cognition in healthy, community-dwelling human participants. We found that the current evidence base is weak with mixed conclusions and multiple methodological caveats, which precludes strong conclusions pertaining to the suitability of dietary recommendations for each food group per age group. Long-term intervention and prospective cohort studies are needed to reduce this knowledge deficit. Revising dietary recommendations with the aim of maintaining an adequate nutrient intake to sustain healthy cognitive function across the life course could be worthwhile. This Review outlines recommendations for future work to help improve the current knowledge deficit regarding dietary intake and cognitive function across the life course and its implications for dietary guidelines such as the EAT-Lancet Commission.