A B S T R A C TMathematical models, such as the DNDC (DeNitrification DeComposition) model, are powerful tools that are increasingly being used to examine the potential impacts of management and climate change in agriculture. DNDC can simulate the processes responsible for production, consumption and transport of nitrous oxide (N 2 O). During the last 20 years DNDC has been modified and adapted by various research groups around the world to suit specific purposes and circumstances. In this paper we review the different versions of the DNDC model including models developed for different ecosystems, e.g. Forest-DNDC, Forest-DNDC-Tropica, regionalised for different areas of the world, e.g. NZ-DNDC, UK-DNDC, modified to suit specific crops, e.g. DNDC-Rice, DNDC-CSW or modularised e.g. Mobile-DNDC, Landscape-DNDC. A 'family tree' and chronological history of the DNDC model is presented, outlining the main features of each version. A literature search was conducted and a survey sent out to c. 1500 model users worldwide to obtain information on the use and development of DNDC. Survey results highlight the many strengths of DNDC including the comparative ease with which the DNDC model can be used and the attractiveness of the graphical user interface. Identified weaknesses could be rectified by providing a more comprehensive user manual, version control and increasing model transparency in collaboration with the Global Research Alliance Modelling Platform (GRAMP), which has much to offer the DNDC user community in terms of promoting the use of DNDC and addressing the deficiencies in the present arrangements for the models' stewardship.

HighlightsHighest GHG emissions from food production are from rice and ruminant products.Highest GHG emissions from consumption are from rice and livestock products.Consumption choice can either increase or decrease total GHG emissions.

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Abstract. The oxygen isotope composition (δ18O) of leaf water (δ18Oleaf) is an important determinant of environmental and physiological information found in biological archives, but the system-scale understanding of the propagation of the δ18O of rain through soil and xylem water to δ18Oleaf has not been verified for grassland. Here we report a unique and comprehensive dataset of fortnightly δ18O observations in soil, stem and leaf waters made over seven growing seasons in a temperate, drought-prone, mixed-species grassland. Using the ecohydrology part of a physically based, 18O-enabled soil–plant–atmosphere transfer model (MuSICA), we evaluated our ability to predict the dynamics of δ18O in soil water, the depth of water uptake, and the effects of soil and atmospheric moisture on 18O enrichment of leaf water (Δ18Oleaf) in this ecosystem. The model accurately predicted the δ18O dynamics of the different ecosystem water pools, suggesting that the model generated realistic predictions of the vertical distribution of soil water and root water uptake dynamics. Observations and model predictions indicated that water uptake occurred predominantly from shallow (<20 cm) soil depths throughout dry and wet periods in all years, presumably due (at least in part) to the effects of high grazing pressure on root system turnover and placement. Δ18Oleaf responded to both soil and atmospheric moisture contents and was best described in terms of constant proportions of unenriched and evaporatively enriched water (two-pool model). The good agreement between model predictions and observations is remarkable as model parameters describing the relevant physical features or functional relationships of soil and vegetation were held constant with one single value for the entire mixed-species ecosystem.

Agriculture is a major contributor to India's environmental footprint, particularly through greenhouse gas (GHG) emissions from livestock and fresh water used for irrigation. These impacts are likely to increase in future as agriculture attempts to keep pace with India's growing population and changing dietary preferences. Within India there is considerable dietary variation, and this study therefore aimed to quantify the GHG emissions and water usage associated with distinct dietary patterns.Five distinct diets were identified from the Indian Migration Study – a large adult population sample in India – using finite mixture modelling. These were defined as: Rice & low diversity, Rice & fruit, Wheat & pulses, Wheat, rice & oils, Rice & meat. The GHG emissions of each dietary pattern were quantified based on a Life Cycle Assessment (LCA) approach, and water use was quantified using Water Footprint (WF) data. Mixed-effects regression models quantified differences in the environmental impacts of the dietary patterns.There was substantial variability between diets: the rice-based patterns had higher associated GHG emissions and green WFs, but the wheat-based patterns had higher blue WFs. Regression modelling showed that the Rice & meat pattern had the highest environmental impacts overall, with 0.77 (95% CI 0.64–0.89) kg CO2e/capita/day (31%) higher emissions, 536 (95% CI 449–623) L/capita/day (24%) higher green WF and 109 (95% CI 85.9–133) L/capita/day (19%) higher blue WF than the reference Rice & low diversity pattern.Diets in India are likely to become more diverse with rising incomes, moving away from patterns such as the Rice & low diversity diet. Patterns such as the Rice & meat diet may become more common, and the environmental consequences of such changes could be substantial given the size of India's population. As global environmental stress increases, agricultural and nutrition policies must recognise the environmental impacts of potential future dietary changes.