Project Leader
Growth model preparatory sequential digs
Determination of dry matter growth and sugar partitioning will be determined from sequential harvests carried out by the Research and Development department of British Sugar plc in 2010. These data will be valuable for independent tests of a future updated Broom's Barn sugar beet growth model.
Member
Adapting the Broom′s Barn sugar beet growth model to todays conditions
The Broom’s Barn sugar beet growth model is an important tool used for providing advice and for research. The model worked well in the past: simulated yields closely matched actual yields across a wide range of environments and soils. However, the model was built and calibrated using data from 1980 to1991, and since then, varieties and management practices have changed. To remain relevant, the model needs updating by tuning its components to improved varieties and management practices, such as widespread use of new fungicides and better seed treatments.
The data to re-tune components of the model are not available in sufficient detail from recent experiments. Therefore new data will be collected in 2011 from five field sampling locations representing different soil textures and differences in water availability. The weather and water inputs at each site will be carefully measured, as will the crop canopy cover, sugar concentration and fresh and dry matter accumulation of foliage and roots. All sites will be sown with XBEET seed of a rhizomania tolerant variety, and all will be treated with synthetic foliar fungicides.
Once re-tuned, the model will be tested against data that are being collected from sequential harvests conducted by British Sugar and fungicides studies carried out at Broom’s Barn to ensure that its output is robust. The outcome will be a refurbished, fully updated model that can be used with confidence by the industry and researchers to underpin advice, decision-making and to help understand how best to maximise productivity and profit from the crop.
Additional value to variety trial data: a performance rating for wheat varieties for dry conditions
In the UK, at least 10% of the wheat yield is lost each year due to insufficient soil moisture, with greater losses on light soils or in very dry years. Therefore, crops frequently fail to attain their potential output because water supply cannot keep pace with demand, often during critical stages of yield formation. Recent record-breaking dry conditions reinforce the need to find ways to maximise productivity when water is limiting. Research has shown that wheat varieties can be fundamentally different in their susceptibility to dry conditions. However, growers have little guidance on which of the current varieties is best suited for dry conditions. The main aim of this work is to help enable the identification of superior wheat varieties for water-limited conditions.
Growers want to know what variety to plant on light land, and which varieties are more likely to yield better in dry conditions. Currently, there are few quantitative data to help guide these decisions. However, the wealth of information contained in the multi-location variety trials conducted each year can be mined with new statistical tools to provide a useful ranking of varieties. The objectives of the proposed work are to evaluate the 2011 RL trial yield data by assigning a drought stress index to each test site using site-specific soil and weather data, then to score each variety according to how well it performed relative to other varieties along a gradient of sites from unstressed to stressed. In addition, the analysis can show which varieties tend to be more stable than others across environments, and which varieties show the best combination of yield potential and yield stability. When these data are combined with environmental variables for each trial, additional information about varieties and test locations can be obtained with little extra cost.
Application of non-linear mathematics and stochastic modelling to complex biological systems
This project aims to develop novel mathematical approaches, based on non-linear mathematics and stochastic modelling, to analyse and predict the behaviour of complex agricultural and biological systems underpinning predictive systems biology. Key objectives of the project are:
1. Modelling impact of climate change
The probability and the magnitude of extreme events and impacts on crops are likely to increase under climate change. We will develop methodology and computational tools to analyse extreme impacts on crops and plant communities under climate change. Specifically:
a) to develop local-scale climate scenarios, based on the LARS-WG Weather Generator, a multi-model ensemble of global and regional climate models
b) to develop a dataset of LARS-WG baseline parameters for Europe with a 25 km grid.
2. Crop modelling
Crop models provide a consistent framework for integrating our understanding of plant processes as influenced by environments. Specifically:
a) to use crop simulation models to deconvolute complex traits, such as nitrogen use efficiency (NUE) or water use efficiency (WUE);
b) to develop computational tools for evaluating performance of new genotypes in diverse environments.
3. Individual-based modelling
The development of resistance in pest insects to insecticides is a significant barrier to sustainable farming. The evolution of resistance is affected by many factors limiting applications of classical modelling approaches. We will develop an individual-based model (IbM) that includes genetic status, individual behaviours, multritrophic interactions and environmental heterogeneity. Specifically:
a) to develop a predictive high-performance IbM;
b) to develop approaches for analysis of stochastic high-dimensional IbM output;
c) to predict of the evolution of resistance in model systems.
Benchmarking beet production performance: a decision support system for individual growers
After the 2006 reform of the EU Sugar Regime, the UK sugar industry is likely to have to match its production more closely to the size of its European market because subsidized exports are likely to be disallowed. In these conditions the industry must be able to plan production more precisely if it is to remain profitable. For the last three years the most important detraction from sugar beet growers' profits has been to consistently produce a surplus of more than 20%. This research by Broom's Barn and British Sugar will produce a decision support system based on independent research, performance as an aid to deciding the area to plant.
Benchmarking grower′s production to the potential yield set by the environment
This proposal is to build a decision support tool to both analyse past production and forecast this seasons’ production for individual farmers, via the internet. To achieve this, the system will have to be tailored to an analysis of the production record of these farmers. Similar, individual-farmer systems are being developed to schedule sugar cane harvest amongst individual paddocks in whole processing regions in Queensland, Australia.
This decision support tool will be built around the Broom’s Barn Crop Growth model. Mathematical models are commonly being used to analyse crop productivity in experiments, but we are not aware of crop models being used elsewhere in the way that we propose. The proposed decision support tool will rely heavily on large databases of production performance, which are available in the sugar industry but are seldom available for other crops. A recent assessment of the skill of models of cereal production from a Defra sponsored project has shown that most are too complex and too detailed to be used in this way. However this is not the case for the proposed sugar beet support model.
Frost protection for beet in the field
If sugar beet freeze sufficiently badly and then thaw, they can no longer be processed to extract sugar. Beet are usually harvested by Christmas and stored in insulated clamps to eliminate the risk of freezing. Even under good storage conditions, sugar beet loose 1.8% of their sugar every 10 days.
The objective of this project is to specify a cheap but cost effective soil covering method that will allow good quality beet to be harvested until late February without major investment in new machines..
The project will determine whether, on sandy or peaty sites, sufficient soil can be ridged up around beet roots during November and December to insulate them from severe frost and prevent damage and post-harvest degradation. A mathematical modelling approach will be used to specify the amount of insulation required.
The project will involve:
1) Evaluation of available data from previous ridging trials
2) A desk study to determine the temperature/duration of cold weather against which frost protection is needed in UK conditions
3) Evaluation, by modelling, of the depth of sandy or peaty soil needed to provide protection.
4) Field trials over three seasons on an organic fen and a sandy site to examine the practicality, cost and effectiveness of covering beet with soil using modified ridging equipment.
5) A field trial in East Anglia to monitor the effect of soil ridging on beet quality during warm and cold periods during the autumn and winter.
6) Modification and development of equipment to remove as much of the ridged soil as possible to minimise problems at harvest.
7) Validation of mathematical models using the collected field data.
8) Use of the sites for grower demonstrations.
High resolution meteorological data to support improved decision making and increased profitability in sugar beet production
The weather has a fundamental influence on crop growth and yield. Modern sugar beet production planning (timing husbandry operations, prediction of crop canopy development, pests and diseases control, soil moisture/irrigation needs, improved local yield forecasts and better processing management and factory operation) already rely on weather data. Despite this reliance, weather data is rarely available immediately and is not sufficiently local to be really valuable.
This research proposes to use modern communication technology, expertise in interpolation (mathematically calculating weather data between met. Stations), and access to a greater network of weather recording stations to provide locally-relevant, real-time and forecast weather data. In addition to weather data from the UK Met. Office, the project will benefit substantially from (a) the roadside network of met. Stations operated by Vaisala TMI and (b) smallemetworks. The combined data will be provided in a system which will help cut production costs, guide decisions on crop husbandry, storage and processing leading to increased sugar yield and industry profitability. Informal estimates of the benefits to the industry are estimated to be equivalent to at least £1 per tonne giving an increased annual margin of almost £10 million to the industry as a whole.
The system will be validated using a sugar beet yield model but also in beet crops remote from meteorological stations. Locally relevant weather data will be generated as a continuous ‘surface’ using new interpolation techniques. These will take account of a range of natural and man-made topographical features when calculating weather data at locations between sites of meteorological recording stations (interpolation). Sugar beet industry users of the system will have access, by a range of means, to locally relevant meteorological data and/or model output.
Eighteen organisations are committed to the Foresight LINK programme.
Improving recommendations and advice for nitrogen use in sugar beet
The Defra Reference Book, RB209, which recommends how fertilisers should be used in UK agriculture, is being revised. For sugar beet, in particular, an analysis of a major series of nitrogen (N) fertilizer experiments in the 1990’s found no relationship between optimum dose of N fertilizer and Soil Mineral N (at sowing or in May) on mineral soils to which no organic manure had been added since the previous summer. Such crops represent more than 65% of that grown in the UK beet. In addition, the current estimates of inorganic N fertilizer use on beet indicates that, on average, applications to the national crop exceed the RB209 recommendation by about 10% (i.e. approximately 11kg N/ha). Clearly we need a new basis for the N fertilizer recommendations for the beet crop for the new version of RB209. The basis of these recommendations must be sufficiently transparent to be convincing to growers, advisors and those using RB209 for regulatory purposes (e.g. Environment Agency).
The project objectives are:
1. To collate and reanalyse all available post-1980 N-response data to bring the N-fertiliser recommendations up to date;
2. To obtain more information on the N uptake of commercial beet crops through (a) a desk-top analysis of field survey and factory tarehouse data and (b) direct sampling and analysis of growers’ crops.
Improving water use efficiency and drought tolerance in UK winter wheats
1)Evaluation of the relative drought tolerance of current wheat varieties using multi-location variety trial data (breeders’ trials and Recommended List Official variety trials).
2)Assessment of genetic diversity for water use efficiency (WUE), drought tolerance and drought-related physiological and morphological traits in a panel of diverse UK lines grown under controlled field drought conditions.
3)Genotype a subset of lines that contrast in responsiveness to water supply using markers for key targets that may affect WUE.
Inputs/Outputs - Effects of increased input costs on crop profitability
During 2007, input costs and returns changed greatly for arable farmers in the UK. Fertiliser prices have doubled pesticides costs have increased at more than twice the rate of inflation and the price of diesel has risen around 50%. These increases, combined with the effect of the sugar regime changes on the returns from sugar beet, are causing growers to question the levels of inputs that they should use. In many cases the increased costs will not alter the inputs required for optimal economic return. However, there are limits to what farmers can afford and, in some cases, an alternative strategy for inputs may be required, if not now then possibly very soon if costs continue to rise.
The BBRO has information on responses of beet to nutrient inputs and on the effects of diseases, pests and weeds on sugar beet yield. However, there is no information to hand for growers to determine how close their current input costs are to the economic optima. In some cases, this may result in them cutting back on inputs unnecessarily such that they reduce their profitability.
The aim of this project is to help growers to maximise yields by providing them with useful models so that they can determine the cost-effectiveness of inputs, to give them confidence to use appropriate inputs and, also, to help the BBRO determine where ‘pinch points’ are likely to occur and where research or development is required.
Interactions between viruses, their vectors and hosts I - Aphid monitoring and infectivity studies
Virus yellows remains an annual threat to the UK sugar industry because the maritime climate favours the overwintering survival of the host plants and the aphid species that transmit these viruses to the crop. The key viruses involved within the virus yellows 'complex' are Beet mild yellowing virus (BMYV) and Beet yellows virus (BYV), while Beet chlorosis virus (BChV), first identified in the UK in the early 1990s, now plays an important role in the epidemiology of the disease. The overall objective of this proposal is to provide the industry with timely advice on appropriate control measures which have optimum effect, are cost effective and are least damaging to the environment.
Early forecasts of the incidence of virus yellows will be issued each year, and during the spring and summer aphid numbers will be monitored using a network of yellow water pans across the growing regions. The infectivity of individual Myzus persicae and Macrosiphum euphorbiae will be determined using the diagnostic methods developed in previous SBREF and BBRO funded programmes. It is also proposed to exploit existing and future aphid / infectivity data to develop real time risk maps and a local decision support system to aid growers at the farm level. The effectiveness of current and future control measures will be determined in collaboration with the agricultural staff at Holmewood Hall. The effect of Beet mosaic virus (BtMV) on the yield of the root crop will be measured in light of the recent increase of this disease in East Anglia, especially within the early part of the growing season.
In this programme, the close links established between the agricultural staff at British Sugar and the virology group at Broom's Barn will be maintained, and the interactions between Broom's Barn and the Rothamsted Insect Survey will also continue to provide the Industry with up-to-date information on the economic impact of virus yellows.
Modelling beet storage conditions and their impact on beet deterioration
It is estimated that each year the UK sugar industry loses a minimum of £5M worth of extractable sugar while beet are being stored on farms prior to being processed. Most of this loss is due to respiration of the beet and regrowth of shoots. Studies which aim to test ways to minimize these loses are difficult and expensive to conduct. An alternative to empirical tests to attempt to optimize storage conditions is to conduct virtual experiments with computer simulations of changes in sugar yield and beet quality during storage. This project will create a model of changes in the internal environment of beet storage piles in relation to changes in the weather conditions so that loses of sugar yield and extractability can be predicted
Modelling beet yields in Sweden
The beet sugar producer/processor joining company in Sweden (SBU) wants to benchmark individual growers' performance at producing beet against the potential set by their environment (soil and weather). This project involves turning the Broom's Barn Crop Growth Model to conditions in S. Sweden and listing it against productivity in trials in growers fields.
Using multi-environment variety trial data to screen for drought tolerance
Insufficient moisture during summer months limits UK sugar beet production more than any other single factor. Climate change models predict that summers will got hotter and drier, giving production areas with deep, water retentive soils a competitive advantage. To maintain productivity under these conditions, new, more drought-tolerant varieties are required. In addition, varieties that are less sensitive to the prevailing moisture supply should exhibit greater site-to-site and year-to-year yield stability, improving management decisions for growers and processors. Currently, breeders are not equipped to make these selections (although in other proposed work we address this issue), and there is no mechanism in place for judging the relative drought performance of varieties entered into official variety trials. In a previous BBRO-funded project, we showed that by assigning a drought stress index (DSI) to each trial location, certain varieties showed significantly better yields when water was limiting, while other showed good performance in the absence of drought, but performed poorly when conditions were dry. This type of information would be extremely useful to farmers, processors and seed companies if included as a standard characterisation of all variety trial entries every year. By evaluating data already gathered in variety trials, additional value is added to this investment. From the previous work we already have a database of soil types for nearly each field used in the UK variety trials. Automatic rainfall gauges at each trial location would eliminate reliance on costly, slow or incomplete weather data sources. The outcome of the proposed work will be a drought tolerance rating for Recommended List varieties. Furthermore, seed companies will be encouraged to take up the procedures for evaluation of in-house variety trials, giving them an additional drought tolerance screening tool to increase the prospect of improved varieties being introduced.
Vulnerability of UK agriculture to extreme events
This project aims to fill knowledge gaps relating to crop production in the UK and extreme weather events as a consequence of climate change. The main objectives are:
1. To develop a set of high resolution daily UKCIP02-based climate change scenarios, suitable for analysis of agricultural extreme events;
2. To identify climatic thresholds having severe impacts on yield, quality and environment for representative crops and to assess the risks that these thresholds will be exceeded under climate change;
3. For selected sites compute extreme events and extreme impact indexes for two contrasting crops: reproductive crops (e.g. wheat) and vegetative crops (e.g. sugar beet). Interpolate those extreme impact indexes over the UK and produce regional risk assessment maps.
4. To identify knowledge gaps on physiological sensitivities, potential pest, disease and weed threats linked to extreme weather and to propose approaches to reduce the impact of extreme weather events.
Rothamsted Insect Survey
