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'.

Pollination and other ecosystem services produced by mobile organisms: a conceptual framework for the effects of land-use change

Many ecosystem services are delivered by organisms that depend on habitats that are segregated spatially or temporally from the location where services are provided. Management of mobile organisms contributing to ecosystem services requires consideration not only of the local scale where services are delivered, but also the distribution of resources at the landscape scale, and the foraging ranges and dispersal movements of the mobile agents. We develop a conceptual model for exploring how one such mobile-agent-based ecosystem service (MABES), pollination, is affected by land-use change, and then generalize the model to other MABES. The model includes interactions and feedbacks among policies affecting land use, market forces and the biology of the organisms involved. Animal-mediated pollination contributes to the production of goods of value to humans such as crops; it also bolsters reproduction of wild plants on which other services or service-providing organisms depend. About one-third of crop production depends on animal pollinators, while 60-90% of plant species require an animal pollinator. The sensitivity of mobile organisms to ecological factors that operate across spatial scales makes the services provided by a given community of mobile agents highly contextual. Services vary, depending on the spatial and temporal distribution of resources surrounding the site, and on biotic interactions occurring locally, such as competition among pollinators for resources, and among plants for pollinators. The value of the resulting goods or services may feed back via market-based forces to influence land-use policies, which in turn influence land management practices that alter local habitat conditions and landscape structure. Developing conceptual models for MABES aids in identifying knowledge gaps, determining research priorities, and targeting interventions that can be applied in an adaptive management context.

Spatial and management factors associated with exposure of smallholder dairy cattle in Tanzania to tick-borne pathogens

A cross-sectional study of serum antibody responses of cattle to tick-borne pathogens (Theileria parva, Theileria mutans, Anaplasma marginale, Babesia bigemina and Babesia bovis) was conducted on smallholder dairy farms in Tanga and Iringa Regions of Tanzania. Seroprevalence was highest for T. parva (48% in Iringa and 23% in Tanga) and B. bigemina (43% in Iringa and 27% in Tanga) and lowest for B. bovis (12% in Iringa and 6% in Tanga). We use spatial and non-spatial models, fitted using classical and Bayesian methods, to explore risk factors associated with seroprevalence. These include both fixed effects (age, grazing history and breeding status) and random effects (farm and local spatial effects). In both regions, seroprevalence for all tick-borne pathogens increased significantly with age. Animals pasture grazed in the 3 months prior to the start of the sampling period were significantly more likely to be seropositive for Theileria spp. and Babesia spp. Pasture grazed animals were more likely to be seropositive than zero-grazed animals for A. marginale, but the relationship was weaker than that observed for the other four pathogens. This study did not detect any significant differences in seroprevalence associated with other management-related variables, including the method or frequency of acaricide application. After adjusting for age, there was weak evidence of localised (< 5 km) spatial correlation in exposure to some of the tick borne diseases. However, this was small compared with the 'farm-effect', suggesting that risk factors specific to the farm were more important than those common to the local neighbourhood. Many animals were seropositive for more than one pathogen and the correlation between exposure to the different pathogens remained after adjusting for the identified risk factors. Identifying the determinants of exposure to multiple tick-borne pathogens and characterizing local variation in risk will assist in the development of more effective control strategies for smallholder dairy farms. (c) 2005 Australian Society for Parasitology Inc. Published by Elsevier Ltd. All rights reserved.

HAWAIIAN SKIRT: an F-box gene that regulates organ fusion and growth in arabidopsis

A fast neutron-mutagenized population of Arabidopsis ( Arabidopsis thaliana) Columbia-0 wild-type plants was screened for floral phenotypes and a novel mutant, termed hawaiian skirt ( hws), was identified that failed to shed its reproductive organs. The mutation is the consequence of a 28 bp deletion that introduces a premature amber termination codon into the open reading frame of a putative F-box protein ( At3g61590). The most striking anatomical characteristic of hws plants is seen in flowers where individual sepals are fused along the lower part of their margins. Crossing of the abscission marker, Pro(PGAZAT):beta-glucuronidase, into the mutant reveals that while floral organs are retained it is not the consequence of a failure of abscission zone cells to differentiate. Anatomical analysis indicates that the fusion of sepal margins precludes shedding even though abscission, albeit delayed, does occur. Spatial and temporal characterization, using Pro(HWS):beta-glucuronidase or Pro(HWS):green fluorescent protein fusions, has identified HWS expression to be restricted to the stele and lateral root cap, cotyledonary margins, tip of the stigma, pollen, abscission zones, and developing seeds. Comparative phenotypic analyses performed on the hws mutant, Columbia-0 wild type, and Pro(35S):HWS ectopically expressing lines has revealed that loss of HWS results in greater growth of both aerial and below-ground organs while overexpressing the gene brings about a converse effect. These observations are consistent with HWS playing an important role in regulating plant growth and development.

A framework for comparing pollinator performance: effectiveness and efficiency

Measuring pollinator performance has become increasingly important with emerging needs for risk assessment in conservation and sustainable agriculture that require multi-year and multi-site comparisons across studies. However, comparing pollinator performance across studies is difficult because of the diversity of concepts and disparate methods in use. Our review of the literature shows many unresolved ambiguities. Two different assessment concepts predominate: the first estimates stigmatic pollen deposition and the underlying pollinator behaviour parameters, while the second estimates the pollinator’s contribution to plant reproductive success, for example in terms of seed set. Both concepts include a number of parameters combined in diverse ways and named under a diversity of synonyms and homonyms. However, these concepts are overlapping because pollen deposition success is the most frequently used proxy for assessing the pollinator’s contribution to plant reproductive success. We analyse the diverse concepts and methods in the context of a new proposed conceptual framework with a modular approach based on pollen deposition, visit frequency, and contribution to seed set relative to the plant’s maximum female reproductive potential. A system of equations is proposed to optimize the balance between idealised theoretical concepts and practical operational methods. Our framework permits comparisons over a range of floral phenotypes, and spatial and temporal scales, because scaling up is based on the same fundamental unit of analysis, the single visit.

The relationship between convection and sea surface temperature on intraseasonal timescales

The relationship between tropical convection, surface fluxes, and sea surface temperature (SST) on intraseasonal timescales has been examined as part of an investigation of the possibility that the intraseasonal oscillation is a coupled atmosphere–ocean phenomenon. The unique feature of this study is that 15 yr of data and the whole region from the Indian Ocean to the Pacific Ocean have been analyzed using lag-correlation analysis and compositing techniques. A coherent relationship between convection, surface fluxes, and SST has been found on intraseasonal timescales in the Indian Ocean, Maritime Continent, and west Pacific regions of the Tropics. Prior to the maximum in convection, there are positive shortwave and latent heat flux anomalies into the surface, followed by warm SST anomalies about 10 days before the convective maximum. Coincident with the convective maximum, there is a minimum in the shortwave flux, followed by a cooling due to increased evaporation associated with enhanced westerly wind stress, leading to negative SST anomalies about 10 days after the convection. The relationships are robust from year to year, including both phases of the El Niño–Southern Oscillation (ENSO) although the eastward extent of the region over which the relationship holds varies with the phase of ENSO, consistent with the variations in the eastward extent of the warm pool and westerly winds. The spatial scale of the anomalies is about 60° longitude, consistent with the scale of the intraseasonal oscillation. The spatial and temporal characteristics of the surface flux and SST perturbations are consistent with the surface flux variations forcing the ocean, and the magnitudes of the anomalies are consistent with mixed-layer depths appropriate to the Indian Ocean and west Pacific

Use of Megasphaera elsdenii NCIMB41125 as a probiotic for early-lactation dairy cows: effects on rumen pH and fermentation patterns

Multiparous rumen-fistulated Holstein cows were fed, from d 1 to 28 post-calving, an ad libitum TMR containing (g/kg DM) grass silage (196), corn silage (196), wheat (277), soybean meal (100), and other feeds (231) with CP, NDF, starch and water soluble carbohydrate concentrations of 176, 260, 299 and 39 g/kg DM respectively and ME of 12.2 MJ/kg DM. Treatments consisting of a minimum of 1010 cfu Megasphaera elsdenii NCIMB 41125 in 250 ml solution (MEGA) or 250 ml of autoclaved M. elsdenii (CONT) were administered via the rumen cannula on d 3 and 12 of lactation (n=7 per treatment). Mid-rumen pH was measured every 15 minutes and eating and ruminating behavior was recorded for 24 h on d 2, 4, 6, 8, 11, 13, 15, 17, 22 and 28. Rumen fluid for VFA and lactic acid (LA) analysis was collected at 11 timepoints on each of d 2, 4, 6, 13 and 15. Data were analysed as repeated measures using the Glimmix (LA data) or Mixed (all other data) procedures of SAS with previous 305 d milk yield and d 2 measurements as covariates where appropriate. Milk yield was higher (CONT 43.0 vs MEGA 45.4 ±0.75 kg/d, P=0.051) and fat concentration was lower (CONT 45.6 vs MEGA 40.4 ±1.05 g/kg, P=0.005) in cows that received MEGA. Time spent eating (263 ±15 min/d) and ruminating (571 ±13 min/d), DM intake (18.4 ±0.74 kg/d), proportion of each 24 h period with rumen pH below 5.6 (3.69 ±0.94 h) and LA concentrations (2.00 mM) were similar (P>0.327) across treatments. Ruminal total VFA concentration (104 ±3 mM) was similar (P=0.404) across treatments, but a shift from acetate (CONT 551 vs MEGA 524 ±14 mmol/mol VFA, P=0.161) to propionate production (CONT 249 vs MEGA 275 ±11 mmol/mol VFA, P=0.099) meant that the acetate:propionate ratio (CONT 2.33 vs MEGA 1.94 ±0.15) was reduced (P=0.072) in cows that received MEGA. This study provides evidence that supplementation of early lactation dairy cows with MEGA alters rumen fermentation patterns in favour of propionate, with potential benefits for animal health and productivity.

Carbon balance of UK peatlands: current state of knowledge and future research challenges

The retention of peatland carbon (C) and the ability to continue to draw down and store C from the atmosphere is not only important for the UK terrestrial carbon inventory, but also for a range of ecosystem services, the landscape value and the ecology and hydrology of ~15% of the land area of the UK. Here we review the current state of knowledge on the C balance of UK peatlands using several studies which highlight not only the importance of making good flux measurements, but also the spatial and temporal variability of different flux terms that characterise a landscape affected by a range of natural and anthropogenic processes and threats. Our data emphasise the importance of measuring (or accurately estimating) all components of the peatland C budget. We highlight the role of the aquatic pathway and suggest that fluxes are higher than previously thought. We also compare the contemporary C balance of several UK peatlands with historical rates of C accumulation measured using peat cores, thus providing a long-term context for present-day measurements and their natural year-on-year variability. Contemporary measurements from 2 sites suggest that current accumulation rates (–56 to –72 g C m–2 yr–1) are at the lower end of those seen over the last 150 yr in peat cores (–35 to –209 g C m–2 yr–1). Finally, we highlight significant current gaps in knowledge and identify where levels of uncertainty are high, as well as emphasise the research challenges that need to be addressed if we are to improve the measurement and prediction of change in the peatland C balance over future decades.

Multi-satellite observed responses of precipitation and its extremes to interannual climate variability

The consistency of precipitation variability estimated from the multiple satellite-based observing systems is assessed. There is generally good agreement between TRMM TMI, SSM/I, GPCP and AMSRE datasets for the inter-annual variability of precipitation since 1997 but the HOAPS dataset appears to overestimate the magnitude of variability. Over the tropical ocean the TRMM 3B42 dataset produces unrealistic variabilitys. Based upon deseasonalised GPCP data for the period 1998-2008, the sensitivity of global mean precipitation (P) to surface temperature (T) changes (dP/dT) is about 6%/K, although a smaller sensitivity of 3.6%/K is found using monthly GPCP data over the longer period 1989-2008. Over the tropical oceans dP/dT ranges from 10-30%/K depending upon time-period and dataset while over tropical land dP/dT is -8 to -11%/K for the 1998-2008 period. Analyzing the response of the tropical ocean precipitation intensity distribution to changes in T we find the wetter area P shows a strong positive response to T of around 20%/K. The response over the drier tropical regimes is less coherent and varies with datasets, but responses over the tropical land show significant negative relationships over an interannual time-scale. The spatial and temporal resolutions of the datasets strongly influence the precipitation responses over the tropical oceans and help explain some of the discrepancy between different datasets. Consistency between datasets is found to increase on averaging from daily to 5-day time-scales and considering a 1o (or coarser) spatial resolution. Defining the wet and dry tropical ocean regime by the 60th percentile of P intensity, the 5-day average, 1o TMI data exhibits a coherent drying of the dry regime at the rate of -20%/K and the wet regime becomes wetter at a similar rate with warming.

Simulating dynamic crop growth with an adapted land surface model – JULES-SUCROS: model development and validation

The increasing demand for ecosystem services, in conjunction with climate change, is expected to signif- icantly alter terrestrial ecosystems. In order to evaluate the sustainability of land and water resources, there is a need for a better understanding of the relationships between crop production, land surface characteristics and the energy and water cycles. These relationships are analysed using the Joint UK Land Environment Simulator (JULES). JULES includes the full hydrological cycle and vegetation effects on the energy, water, and carbon fluxes. However, this model currently only simulates land surface processes in natural ecosystems. An adapted version of JULES for agricultural ecosystems, called JULES-SUCROS has therefore been developed. In addition to overall model improvements, JULES-SUCROS includes a dynamic crop growth structure that fully fits within and builds upon the biogeochemical modelling framework for natural vegetation. Specific agro-ecosystem features such as the development of yield-bearing organs and the phenological cycle from sowing till harvest have been included in the model. This paper describes the structure of JULES-SUCROS and evaluates the fluxes simulated with this model against FLUXNET measurements at 6 European sites. We show that JULES-SUCROS significantly improves the correlation between simulated and observed fluxes over cropland and captures well the spatial and temporal vari- ability of the growth conditions in Europe. Simulations with JULES-SUCROS highlight the importance of vegetation structure and phenology, and the impact they have on land–atmosphere interactions.

Generic unified modelling process for developing semantically rich, dynamic and temporal models

Models play a vital role in supporting a range of activities in numerous domains. We rely on models to support the design, visualisation, analysis and representation of parts of the world around us, and as such significant research effort has been invested into numerous areas of modelling; including support for model semantics, dynamic states and behaviour, temporal data storage and visualisation. Whilst these efforts have increased our capabilities and allowed us to create increasingly powerful software-based models, the process of developing models, supporting tools and /or data structures remains difficult, expensive and error-prone. In this paper we define from literature the key factors in assessing a model’s quality and usefulness: semantic richness, support for dynamic states and object behaviour, temporal data storage and visualisation. We also identify a number of shortcomings in both existing modelling standards and model development processes and propose a unified generic process to guide users through the development of semantically rich, dynamic and temporal models.