Translating climate forecasts into agricultural terms: advances and challenges

Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate'.

Translating climate forecasts into agricultural terms: advances and challenges

Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate'.

Development and assessment of a coupled crop-climate model

It is well established that crop production is inherently vulnerable to variations in the weather and climate. More recently the influence of vegetation on the state of the atmosphere has been recognized. The seasonal growth of crops can influence the atmosphere and have local impacts on the weather, which in turn affects the rate of seasonal crop growth and development. Considering the coupled nature of the crop-climate system, and the fact that a significant proportion of land is devoted to the cultivation of crops, important interactions may be missed when studying crops and the climate system in isolation, particularly in the context of land use and climate change. To represent the two-way interactions between seasonal crop growth and atmospheric variability, we integrate a crop model developed specifically to operate at large spatial scales (General Large Area Model for annual crops) into the land surface component of a global climate model (GCM; HadAM3). In the new coupled crop-climate model, the simulated environment (atmosphere and soil states) influences growth and development of the crop, while simultaneously the temporal variations in crop leaf area and height across its growing season alter the characteristics of the land surface that are important determinants of surface fluxes of heat and moisture, as well as other aspects of the land-surface hydrological cycle. The coupled model realistically simulates the seasonal growth of a summer annual crop in response to the GCM's simulated weather and climate. The model also reproduces the observed relationship between seasonal rainfall and crop yield. The integration of a large-scale single crop model into a GCM, as described here, represents a first step towards the development of fully coupled crop and climate models. Future development priorities and challenges related to coupling crop and climate models are discussed.

The effect of paddock size on the response of seasonal anoestrous ewes to the ram effect

Many aspects of the conditions required to maximize the ewe's response to ram introduction in the late anoestrous season remain unclear. The aim of this research was to determine whether grazing space allowances could influence the efficacy of the ram effect. In August 1995, at Reading (latitude 51degrees27'N), following a 3-month isolation period from rams, two groups of nulliparous Mule ewes, aged 15 months, were introduced to four rains in a low (12 ewes/ha; treatment L, n = 124) or in a high stocking rate (84 ewes/ha; treatment H, n = 126). From the beginning of August until the end of August oestrous behaviour was recorded by daily checks of mating marks on ewes. Rams were removed and in October all ewes were scanned (day 50) for pregnancy. No significant differences were found in the parameters investigated. Eighty-two percent of the L and 75.4% of the H ewes exhibited oestrus, with a pronounced peak on day 23 following ram introduction and a compact concentration in the 21-25-day period. The oestrous synchronisation rate in this 5-day period was 69.4 and 68.3%, respectively for L and H. The mean interval from ram introduction to oestrus was 23.17+/-2.4 days in L and 23.0+/-2.2 days in the H group. Conception rates were 84.3 and 87.4% for L and H groups, respectively. These results suggest that the response of anoestrous ewes to the introduction of rams was not affected by grazing space allowances and that yearling Mule ewes respond well to the ram effect in the late anoestrus season. (C) 2003 Elsevier Science B.V. All rights reserved.

Adaptation to climate change and variability: farmer responses to intra-seasonal precipitation trends in South Africa

We describe the nature of recent (50 year) rainfall variability in the summer rainfall zone, South Africa, and how variability is recognised and responded to on the ground by farmers. Using daily rainfall data and self-organising mapping (SOM) we identify 12 internally homogeneous rainfall regions displaying differing parameters of precipitation change. Three regions, characterised by changing onset and timing of rains, rainfall frequencies and intensities, in Limpopo, North West and KwaZulu Natal provinces, were selected to investigate farmer perceptions of, and responses to, rainfall parameter changes. Village and household level analyses demonstrate that the trends and variabilities in precipitation parameters differentiated by the SOM analysis were clearly recognised by people living in the areas in which they occurred. A range of specific coping and adaptation strategies are employed by farmers to respond to climate shifts, some generic across regions and some facilitated by specific local factors. The study has begun to understand the complexity of coping and adaptation, and the factors that influence the decisions that are taken.

Intra- and interspecific variations in vacuolar flavonoids among Ficus species from the Budongo Forest, Uganda

Leaves of 14 species of Ficus growing in the Budongo Forest, Uganda, were analysed for vacuolar flavonoids. Three to six accessions were studied for each species to see whether there was intraspecific chemical variation. Thirty-nine phenolic compounds were identified or characterised, including 14 flavonol O-glycosides, six flavone O-glycosides and 15 flavone C-glycosides. In some species the flavonoid glycosides were acylated. Ficus thonningii contained in addition four stilbenes including glycosides. Most of the species could be distinguished from each other on the basis of their flavonoid profiles, apart from Ficus sansibarica and Ficus saussureana, which showed a very strong intraspecific variation. However, on the whole flavonoid profiles were sufficiently distinct to help in future identifications.

The seasonal forecast of electricity demand: A hierarchical Bayesian model with climatological weather generator

In this paper we focus on the one year ahead prediction of the electricity peak-demand daily trajectory during the winter season in Central England and Wales. We define a Bayesian hierarchical model for predicting the winter trajectories and present results based on the past observed weather. Thanks to the flexibility of the Bayesian approach, we are able to produce the marginal posterior distributions of all the predictands of interest. This is a fundamental progress with respect to the classical methods. The results are encouraging in both skill and representation of uncertainty. Further extensions are straightforward at least in principle. The main two of those consist in conditioning the weather generator model with respect to additional information like the knowledge of the first part of the winter and/or the seasonal weather forecast. Copyright (C) 2006 John Wiley & Sons, Ltd.

The development of ISSR-derived SCAR markers around the Seasonal Flowering Locus (SFL) in fragaria vesca

Fragaria vesca is a short-lived perennial with a seasonal-flowering habit. Seasonality of flowering is widespread in the Rosaceae and is also found in the majority of temperate polycarpic perennials. Genetic analysis has shown that seasonal flowering is controlled by a single gene in F. vesca, the SEASONAL FLOWERING LOCUS (SFL). Here, we report progress towards the marker-assisted selection and positional cloning of SFL, in which three ISSR markers linked to SFL were converted to locus-specific sequence-characterized amplified region (SCAR1–SCAR3) markers to allow large-scale screening of mapping progenies. We believe this is the first study describing the development of SCAR markers from ISSR profiles. The work also provides useful insight into the nature of polymorphisms generated by the ISSR marker system. Our results indicate that the ISSR polymorphisms originally detected were probably caused by point mutations in the positions targeted by primer anchors (causing differential PCR failure), by indels within the amplicon (leading to variation in amplicon size) and by internal sequence differences (leading to variation in DNA folding and so in band mobility). The cause of the original ISSR polymorphism was important in the selection of appropriate strategies for SCAR-marker development. The SCAR markers produced were mapped using a F. vesca f. vesca × F. vesca f. semperflorens testcross population. Marker SCAR2 was inseparable from the SFL, whereas SCAR1 mapped 3.0 cM to the north of the gene and SCAR3 1.7 cM to its south.