Climate change and the flowering time of annual crops

Crop production is inherently sensitive to variability in climate. Temperature is a major determinant of the rate of plant development and, under climate change, warmer temperatures that shorten development stages of determinate crops will most probably reduce the yield of a given variety. Earlier crop flowering and maturity have been observed and documented in recent decades, and these are often associated with warmer (spring) temperatures. However, farm management practices have also changed and the attribution of observed changes in phenology to climate change per se is difficult. Increases in atmospheric [CO2] often advance the time of flowering by a few days, but measurements in FACE (free air CO2 enrichment) field-based experiments suggest that elevated [CO2] has little or no effect on the rate of development other than small advances in development associated with a warmer canopy temperature. The rate of development (inverse of the duration from sowing to flowering) is largely determined by responses to temperature and photoperiod, and the effects of temperature and of photoperiod at optimum and suboptimum temperatures can be quantified and predicted. However, responses to temperature, and more particularly photoperiod, at supraoptimal temperature are not well understood. Analysis of a comprehensive data set of time to tassel initiation in maize (Zea mays) with a wide range of photoperiods above and below the optimum suggests that photoperiod modulates the negative effects of temperature above the optimum. A simulation analysis of the effects of prescribed increases in temperature (0-6 degrees C in + 1 degrees C steps) and temperature variability (0% and + 50%) on days to tassel initiation showed that tassel initiation occurs later, and variability was increased, as the temperature exceeds the optimum in models both with and without photoperiod sensitivity. However, the inclusion of photoperiod sensitivity above the optimum temperature resulted in a higher apparent optimum temperature and less variability in the time of tassel initiation. Given the importance of changes in plant development for crop yield under climate change, the effects of photoperiod and temperature on development rates above the optimum temperature clearly merit further research, and some of the knowledge gaps are identified herein.

Mitigation options for sediment and phosphorus loss from winter-sown arable crops

Sediment and P inputs to freshwaters from agriculture are a major problem in the United Kingdom (UK). This study investigated mitigation options for diffuse pollution losses from arable land. Field trials were undertaken at the hillslope scale over three winters at three UK sites with silt (Oxyaquic Hapludalf), sand (Udic Haplustept), and clay (Typic Haplaquept) soils. None of the mitigation treatments was effective in every year trialled, but each showed overall average reductions in losses. Over five site years, breaking up the compaction in tramlines (tractor wheel tracks) using a tine reduced losses of sediment and P to losses similar to those observed from areas without tramlines, with an average reduction in P loss of 1.06 kg TP ha(-1) Compared to traditional plowing, TP losses under minimum tillage were reduced by 0.30 kg TT ha(-1) over five site years, TP losses under contour cultivation were reduced by 0.30 kg TP ha(-1) over two site years, and TP losses using in-field barriers were reduced by 0.24 kg TP ha(-1) over two site years. In one site year, reductions in losses due to crop residue incorporation were nor significant. Each of the mitigation options trialled. is associated with a small cost at the farm-scale of up to 5 pound ha(-1), or with cost savings. The results indicate that each of the treatments his the potential to be a cost-effective mitigation option, but that tramline management is the most promising treatment, because tramlines dominate sediment and P transfer in surface runoff from arable hillslopes.

Predicting the profile of nutrients available for absorption: from nutrient requirement to animal response and environmental impact

Current feed evaluation systems for dairy cattle aim to match nutrient requirements with nutrient intake at pre-defined production levels. These systems were not developed to address, and are not suitable to predict, the responses to dietary changes in terms of production level and product composition, excretion of nutrients to the environment, and nutrition related disorders. The change from a requirement to a response system to meet the needs of various stakeholders requires prediction of the profile of absorbed nutrients and its subsequent utilisation for various purposes. This contribution examines the challenges to predicting the profile of nutrients available for absorption in dairy cattle and provides guidelines for further improved prediction with regard to animal production responses and environmental pollution. The profile of nutrients available for absorption comprises volatile fatty acids, long-chain fatty acids, amino acids and glucose. Thus the importance of processes in the reticulo-rumen is obvious. Much research into rumen fermentation is aimed at determination of substrate degradation rates. Quantitative knowledge on rates of passage of nutrients out of the rumen is rather limited compared with that on degradation rates, and thus should be an important theme in future research. Current systems largely ignore microbial metabolic variation, and extant mechanistic models of rumen fermentation give only limited attention to explicit representation of microbial metabolic activity. Recent molecular techniques indicate that knowledge on the presence and activity of various microbial species is far from complete. Such techniques may give a wealth of information, but to include such findings in systems predicting the nutrient profile requires close collaboration between molecular scientists and mathematical modellers on interpreting and evaluating quantitative data. Protozoal metabolism is of particular interest here given the paucity of quantitative data. Empirical models lack the biological basis necessary to evaluate mitigation strategies to reduce excretion of waste, including nitrogen, phosphorus and methane. Such models may have little predictive value when comparing various feeding strategies. Examples include the Intergovernmental Panel on Climate Change (IPCC) Tier II models to quantify methane emissions and current protein evaluation systems to evaluate low protein diets to reduce nitrogen losses to the environment. Nutrient based mechanistic models can address such issues. Since environmental issues generally attract more funding from governmental offices, further development of nutrient based models may well take place within an environmental framework.

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'.

Vascular plant diversity and climate change in the alpine zone of the Lefka Ori, Crete

The aim of this study is to analyse the vascular flora and the local climate along an altitudinal gradient in the Lefka Ori massif Crete and to evaluate the potential effects of climate change on the plant diversity of the sub-alpine and alpine zones. It provides a quantitative/qualitative analysis of vegetation-environment relationships for four summits along an altitude gradient on the Lefka Ori massif Crete (1664-2339 m). The GLORIA multi-summit approach was used to provide vegetation and floristic data together with temperature records for every summit. Species richness and species turnover was calculated together with floristic similarity between the summits. 70 species were recorded, 20 of which were endemic, belonging to 23 different families. Cretan endemics dominate at these high altitudes. Species richness and turnover decreased with altitude. The two highest summits showed greater floristic similarity. Only 20% of the total flora recorded reaches the highest summit while 10% is common among summits. Overall there was a 4.96 degrees C decrease in temperature along the 675 m gradient. Given a scenario of temperature increase the ecotone between the sub-alpine and alpine zone would be likely to have the greatest species turnover. Southern exposures are likely to be invaded first by thermophilous species while northern exposures are likely to be more resistant to changes. Species distribution shifts will also depend on habitat availability. Many, already threatened, local endemic species will be affected first.

Climate vs. soil factors in local adaptation of two common plant species

Evolutionary theory suggests that divergent natural selection in heterogeneous environments can result in locally adapted plant genotypes. To understand local adaptation it is important to study the ecological factors responsible for divergent selection. At a continental scale, variation in climate can be important while at a local scale soil properties could also play a role. We designed an experiment aimed to disentangle the role of climate and ( abiotic and biotic) soil properties in local adaptation of two common plant species. A grass (Holcus lanatus) and a legume ( Lotus corniculatus), as well as their local soils, were reciprocally transplanted between three sites across an Atlantic-Continental gradient in Europe and grown in common gardens in either their home soil or foreign soils. Growth and reproductive traits were measured over two growing seasons. In both species, we found significant environmental and genetic effects on most of the growth and reproductive traits and a significant interaction between the two environmental effects of soil and climate. The grass species showed significant home site advantage in most of the fitness components, which indicated adaptation to climate. We found no indication that the grass was adapted to local soil conditions. The legume showed a significant home soil advantage for number of fruits only and thus a weak indication of adaptation to soil and no adaptation to climate. Our results show that the importance of climate and soil factors as drivers of local adaptation is species-dependent. This could be related to differences in interactions between plant species and soil biota.

Climate change and coastal waterbird populations – past declines and future impacts

Considerable attention has been given to the impact of climate change on avian populations over the last decade. In this paper we examine two issues with respect to coastal bird populations in the UK: (1) is there any evidence that current populations are declining due to climate change, and (2) how might we predict the response of populations in the future? We review the cause of population decline in two species associated with saltmarsh habitats. The abundance of Common Redshank Tringa totanus breeding on saltmarsh declined by about 23% between the mid-1980s and mid-1990s, but the decline appears to have been caused by an increase in grazing pressure. The number of Twite Carduelis flavirostris wintering on the coast of East Anglia has declined dramatically over recent decades; there is evidence linking this decline with habitat loss but a causal role for climate change is unclear. These examples illustrate that climate change could be having population-level impacts now, but also show that it is dangerous to become too narrowly focused on single issues affecting coastal birds. Making predictions about how populations might respond to future climate change depends on an adequate understanding of important ecological processes at an appropriate spatial scale. We illustrate this with recent work conducted on the Icelandic population of Black-tailed Godwits Limosa limosa islandica that shows large-scale regulatory processes. Most predictive models to date have focused on local populations (single estuary or a group of neighbouring estuaries). We discuss the role such models might play in risk assessment, and the need for them to be linked to larger-scale ecological processes. We argue that future work needs to focus on spatial scale issues and on linking physical models of coastal environments with important ecological processes.

Effective livelihood adaptation to climate change disturbance: scale dimensions of practice in Mozambique

Natural resource-dependent societies in developing countries are facing increased pressures linked to global climate change. While social-ecological systems evolve to accommodate variability, there is growing evidence that changes in drought, storm and flood extremes are increasing exposure of currently vulnerable populations. In many countries in Africa, these pressures are compounded by disruption to institutions and variability in livelihoods and income. The interactions of both rapid and slow onset livelihood disturbance contribute to enduring poverty and slow processes of rural livelihood renewal across a complex landscape. We explore cross-scale dynamics in coping and adaptation response, drawing on qualitative data from a case study in Mozambique. The research characterises the engagements across multiple institutional scales and the types of agents involved, providing insight into emergent conditions for adaptation to climate change in rural economies, The analysis explores local responses to climate shocks, food security and poverty reduction, through informal institutions, forms of livelihood diversification and collective land-use systems that allow reciprocity, flexibility and the ability to buffer shocks. However, the analysis shows that agricultural initiatives have helped to facilitate effective livelihood renewal, through the reorganisation of social institutions and opportunities for communication, innovation and micro-credit. Although there are challenges to mainstreaming adaptation at different scales, this research shows why it is critical to assess how policies can protect conditions for emergence of livelihood transformation. (C) 2008 Elsevier Ltd. All rights reserved.

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.

Influence of vegetation on the local climate and hydrology in the tropics: sensitivity to soil parameters

Land use change with accompanying major modifications to the vegetation cover is widespread in the tropics, due to increasing demands for agricultural land, and may have significant impacts on the climate. This study investigates (1) the influence of vegetation on the local climate in the tropics; (2) how that influence varies from region to region; and (3) how the sensitivity of the local climate to vegetation, and hence land use change, depends on the hydraulic characteristics of the soil. A series of idealised experiments with the Hadley Centre atmospheric model, HadAM3, are described in which the influence of vegetation in the tropics is assessed by comparing the results of integrations with and without tropical vegetation. The sensitivity of the results to the soil characteristics is then explored by repeating the experiments with a differing, but equally valid, description of soil hydraulic parameters. The results have shown that vegetation has a significant moderating effect on the climate throughout the tropics by cooling the surface through enhanced latent heat fluxes. The influence of vegetation is, however, seasonally dependent, with much greater impacts during the dry season when the availability of surface moisture is limited. Furthermore, there are significant regional variations both in terms of the magnitude of the cooling and in the response of the precipitation. Not all regions show a feedback of vegetation on the local precipitation; this result has been related both to vegetation type and to the prevailing meteorological conditions. An important finding has been the sensitivity of the results to the specification of the soil hydraulic parameters. The introduction of more freely draining soils has changed the soil-moisture contents of the control, vegetated system and has reduced, significantly, the climate sensitivity to vegetation and by implication, land use change. Changes to the soil parameters have also had an impact on the soil hydrology and its interaction with vegetation, by altering the partitioning between fast and slow runoff processes. These results raise important questions about the representation of highly heterogeneous soil characteristics in climate models, as well as the potential influence of land use change on the soil characteristics themselves.