TuberZone: Development of an innovative spatial crop model and decision support system for improved potato agronomy

Agriculture is now a data-rich environment. A multitude of proximal & remote sensors capture many different aspects of agriculture production systems, particularly cropping systems. Nowadays, growers are able to record & change the rates of most agronomic inputs or operations. However, growers rarely use the capabilities at their disposal because they are unable to translate the available data streams into information & then into good agronomic decisions. Incorrect analysis generates incorrect decisions. Because of this, growers are wary to adopt decisions based on information that they do not understand well.

One clear, potentially very important way in which these spatial data can be used is within crop models. Crop models are invaluable to the agricultural community to predict how crops develop under different scenarios (alternative management and/or evolving in-season climate variations). While many well developed & well credential crop models exist, these are built on an assumption of modelling a point, which is an average response for a field or farm. They are not designed for high-resolution spatial modelling & usually collapse when used as such.

The objective for this project is to integrate a point crop model with spatial data to generate an effective spatial crop model for potato production. This will have an emphasis on predicting tuber size distribution (TSD) & managing the various drivers (environmental & managerial) of TSD. By empowering an existing crop model with spatial information, it is possible to remove the grower/agronomist directly from the data analysis & the decision-making. Expert knowledge will be captured within the crop model, but there is no direct involvement between the spatial data & the end-users, removing this source of error and confusion. The spatial crop model is therefore a method for spatial data-fusion & value-adds to the original spatial data. The model provides a relatively simple integrated spatial output (recommended variable-rate management operations) that the grower can access for adoption. The modelling also allows estimates of uncertainty (as well as an operation) to assist growers in risk assessment with differential management. From an academic perspective, a few issues need to be researched & developed to achieve this. These include;
1) Filling the knowledge gap on the amount (magnitude & spatial structure) of crop variability in potato fields. There are very few spatial studies available & this information is needed to correctly parameterise any spatial model within sensible boundary limits.
2) Understanding the drivers of the observed variation in crop production. The variability observed can be linked to spatial information on soil & weather variations, as well as management decisions. This helps to inform the spatial model of the yield determinant factors.
3) Development of a spatial meta-model. The spatial crop model relies on the output from an existing point crop model being used as an input into a spatial meta-model. The spatial meta-model is a new concept. It requires standardisation of inputs, particularly in regards their spatial footprint, correct parameterisation of neighbourhood interactions & correct modelling of the uncertainty at each point in the spatial model. Correct data processing & the knowledge from Points 1) & 2) above will ensure that the meta-model is correctly designed & populated. It will be validated against field experiments in the latter stages of the project.

The project brings together leading UK industry expertise in potato production (SAC, SRUC, McCains), supply chains & processing (McCains), machinery for potato production (Grimme) & precision agricultural services (SE), as well as leading academic researchers in the area of precision agriculture (Newcastle Uni) & crop modelling (Newcastle Uni, Mylnefield Research Services). This consortium is well placed to deliver the project & deliver it to the needs of the industry.

Please see information in summary section.

THE COMMERCIAL PROJECT PARTNERS: The outcome from the project is to achieve a more efficient production system for potato with a more uniform quality of production.
SoilEssentials (SE) will benefit from increased business & company expansion into agronomic service (information) provision. This expands on & grows their core business model of hardware & data sales.
McCains (MC) will benefit from an increase in the potato quality supplied to their processing plants. This reduces waste & optimises return per ton of potato for McCains.
Grimme (GR) is a major potato hardware manufacturer. Increased use of agri-technologies in potato provides a new market opportunity for them to expand their engineering & hardware business. This is conditional on growers being able to make good decisions, which a successful project will achieve.

AGRONOMISTS (AG) Industry agronomists will benefit from being able to provide better service & a point of difference to other companies

ACADEMIC PARTNERS: This marriage of spatial modelling & soft-computing processes with an existing crop model has the potential to add considerable power & flexibility to the original point-based crop model. This is the first proposal of this kind that we are aware of & will significantly enhance the reputation of Newcastle Uni in this area.

POTATO PRODUCERS: Extension of the spatial crop model into spatial decision support systems, & service delivery by SE & AG, will have a major impact on potato growers. It will provide timely, & spatially-explicit information on their production system to allow them to react spatially to the evolving growing season. This will change management radically from a whole-field perspective to a site- or zone-specific in-field perspective.

POTATO INDUSTRY SUPPLY CHAIN: Improved modelling & improved quality on-farm will have flow on effects to the supply-chain. Growers will have a better estimation of their total crop (including quality), which will aid in the logistics of harvest, transport, processing & marketing of the crop. Processers & end-users of potato products will be able to budget with more certainty on production levels.

AGRONOMIC CROP MODELLERS: The conceptual spatial meta-model will be developed in a potato system; however, there is no restriction on the type of system that this could be applied to. The only limitations are the availability of relevant spatial information layers & existing knowledge of the expected variance in crop production & the drivers of this variance. In many systems, particularly cereal systems, the base knowledge & spatial data sets are well understood & this spatial meta-model should be easily adapted to a variety of existing cereal crop models.

ARABLE & HORTICULTURAL SECTOR: Adaption & deployment of the spatial meta-model to other crop models will see a shift from single input crop models to multi-input models & more integrated decision support systems in various cropping systems. This will drive production efficiencies in almost all major arable/horticultural systems. Where the spatial knowledge of crop variability & the drivers of variability are missing, the potential of these crop models will drive the generation of this knowledge. A better understanding of spatial & temporal variation in a production system will have benefits with & without the modelling. Crop models are particularly important in high value specialty crops, where quality premiums, rather than quantity, are key determinants of profitability.

ENVIRONMENTAL BENEFITS: Improved production efficiencies, by differential management based on correct spatial modelling, will drive a reduction in non-point pollution from these production systems. Targeting the right amount of input & the right timing of application maximises the use of the input by the crop & minimises losses. This will have multiple environmental benefits, from reduced volatilisation of GHGs to less leachate in ground & surface waters.