Crop models are essential tools for assessing the threat of climate change to local and global food production(1). Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature(2). Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 degrees C to 32 degrees C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each degrees C of further temperature increase and become more variable over space and time.

Rising Temperatures Reduce Global Wheat Production

Regional phenology is important in ecosystem simulation models and coupled biosphere/atmosphere models. In the continental United States, the timing of the onset of greenness in the spring (leaf expansion, grass green-up) and offset of greenness in the fall (leaf abscission, cessation of height growth, grass brown-oft) are strongly influenced by meteorological and climatological conditions. We developed predictive phenology models based on traditional phenology research using commonly available meteorological and climatological data. Predictions were compared with satellite phenology observations at numerous 20 km x 20 km contiguous landcover sites. Onset mean absolute error was 7.2 days in the deciduous broadleaf forest (DBF) biome and 6.1 days in the grassland biome. Offset mean absolute error was 5.3 days in the DBF biome and 6.3 days in the grassland biome. Maximum expected errors at a 95% probability level ranged from 10 to 14 days. Onset was strongly associated with temperature summations in both grassland and DBF biomes; DBF offset was best predicted with a photoperiod function, while grassland offset required a combination of precipitation and temperature controls. A long-term regional test of the DBF onset model captured field-measured interannual variability trends in lilac phenology. Continental application of the phenology models for 1990-1992 revealed extensive interannual variability in onset and offset. Median continental growing season length ranged from a low of 129 days in 1991 to a high of 146 days in 1992. Potential uses of the models include regulation of the timing and length of the growing season in large-scale biogeochemical models and monitoring vegetation response to interannual climatic variability.

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A continental phenology model for monitoring vegetation responses to interannual climatic variability

Increases in the concentrations of greenhouse gases, carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and halocarbons in the atmosphere due to human activities are associated with global climate change. The concentration of N2O has increased by 16% since 1750. Although the atmospheric concentration of N2O is much smaller (314 ppb in 1998) than of CO2 (365 ppm), its global warming potential (cumulative radiative forcing) is 296 times that of the latter in a 100-year time horizon. Currently, it contributes about 6% of the overall global warming effect but its contribution from the agricultural sector is about 16%. Of that, almost 80% of N2O is emitted from Australian agricultural lands, originating from N fertilisers (32%), soil disturbance (38%), and animal waste (30%). Nitrous oxide is primarily produced in soil by the activities of microorganisms during nitrification, and denitrification processes. The ratio of N2O to N2 production depends on oxygen supply or water-filled pore space, decomposable organic carbon, N substrate supply, temperature, and pH and salinity. N2O production from soil is sporadic both in time and space, and therefore, it is a challenge to scale up the measurements of N2O emission from a given location and time to regional and national levels. Estimates of N2O emissions from various agricultural systems vary widely. For example, in flooded rice in the Riverina Plains, N2O emissions ranged from 0.02% to 1.4% of fertiliser N applied, whereas in irrigated sugarcane crops, 15.4% of fertiliser was lost over a 4-day period. Nitrous oxide emissions from fertilised dairy pasture soils in Victoria range from 6 to 11 kg N2O-N/ha, whereas in arable cereal cropping, N2O emissions range from <0.01% to 9.9% of N fertiliser applications. Nitrous oxide emissions from soil nitrite and nitrates resulting from residual fertiliser and legumes are rarely studied but probably exceed those from fertilisers, due to frequent wetting and drying cycles over a longer period and larger area. In ley cropping systems, significant N2O losses could occur, from the accumulation of mainly nitrate-N, following mineralisation of organic N from legume-based pastures. Extensive grazed pastures and rangelands contribute annually about 0.2 kg N/ha as N2O (93 kg/ha per year CO2-equivalent). Tropical savannas probably contribute an order of magnitude more, including that from frequent fires. Unfertilised forestry systems may emit less but the fertilised plantations emit more N2O than the extensive grazed pastures. However, currently there are limited data to quantify N2O losses in systems under ley cropping, tropical savannas, and forestry in Australia. Overall, there is a need to examine the emission factors used in estimating national N2O emissions; for example, 1.25% of fertiliser or animal-excreted N appearing as N2O (IPCC 1996). The primary consideration for mitigating N2O emissions from agricultural lands is to match the supply of mineral N (from fertiliser applications, legume-fixed N, organic matter, or manures) to its spatial and temporal needs by crops/pastures/trees. Thus, when appropriate, mineral N supply should be regulated through slow-release (urease and/or nitrification inhibitors, physical coatings, or high C/N ratio materials) or split fertiliser application. Also, N use could be maximised by balancing other nutrient supplies to plants. Moreover, non-legume cover crops could be used to take up residual mineral N following N-fertilised main crops or mineral N accumulated following legume leys. For manure management, the most effective practice is the early application and immediate incorporation of manure into soil to reduce direct N2O emissions as well as secondary emissions from deposition of ammonia volatilised from manure and urine. Current models such as DNDC and DAYCENT can be used to simulate N2O production from soil after parameterisation with the local data, and appropriate modification and verification against the measured N2O emissions under different management practices.

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Nitrous oxide emission from Australian agricultural lands and mitigation options: a review

Preface Part I. Farming Systems and Their Biological Components: 1. Agricultural systems 2. Trophic chains 3. Community concepts 4. Genetic resources 5. Development Part II. Physical and Chemical Environments: 6. Aerial environment 7. Soil resources Part III. Production Processes: 8. Nitrogen processes 9. Water relations 10. Photosynthesis 11. Respiration and partitioning Part IV. Resource Management: 12. Soil management 13. Strategies and tactics for rainfed agriculture 14. Water management in irrigated agriculture 15. Energy and labor Part V. Farming, Then, Now and in the Future: 16. Evolution of wheat farming systems in southern Australia 17. Technological change in high-yield agriculture 18. The future of agriculture Species list Conversions and constants useful in crop ecology Index.

Crop Ecology: Productivity and Management in Agricultural Systems

GRAZPLAN: Decision support systems for Australian grazing enterprises—II. The animal biology model for feed intake, production and reproduction and the GrazFeed DSS

Predicting ovulation rate from liveweight in ewes

A simulation model of the development, growth and yield of the wheat crop

A simulation model of a breeding ewe flock

A model of the life cycle of sheep nematodes and the epidemiology of nematodiasis in sheep

Crops of wheat (Triticurn aestivum cv. Olympic) were sown after a 10-month fallow at three times in both 1979 and 1980 in the Wimmera district of Victoria. Total above-ground plant material (biomass) and soil water content were measured for each crop at monthly intervals from sowing to anthesis and thereafter every 2 weeks. The duration of the phenophase, sowing to anthesis, varied from 88 to 163 days, but the maximum difference between anthesis date for the early (May) and late (August) sown crops was only 21 days. The duration of this phenophase was best described by a photothermal unit of 6846 day-degree-hours (>2OC, >6 h). The pattern of biomass accumulation varied markedly between crops, with biomass ranging from 9 to 13 t ha-' and yield between 3 and 4 t ha-'. Total wateruse efficiency in the production of biomass to anthesis ranged from 30.5 to 19.8 kg ha-' mm-' and in the seasonal production of grain from 8.6 to 6.6 kg ha-' mm-'. Whilst the data include only one early sown crop, it was possible to identify an optimum balance between pre- and post-anthesis growth in crops sown in June to produce 9 t ha-' of biomass at anthesis in early November, a yield sink of 12500 grains m-2 and in the 2 years of the experiment a grain yield of 4 t ha-'.

D.H. White

Papers

Predicting ovulation rate from liveweight in ewes

A simulation model of the development, growth and yield of the wheat crop

A simulation model of a breeding ewe flock

A model of the life cycle of sheep nematodes and the epidemiology of nematodiasis in sheep

Effect of sowing time on growth, yield and water-use of rain-fed wheat in the Wimmera, Vic.