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

TuberZone：开发创新的空间作物模型和决策支持系统以改善马铃薯农学

• 批准号: BB/M028984/1
• 负责人: James Taylor
• 金额: $ 40.65万
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FM028984%2F1
• 关键词: TuberZone; Development; innovative; spatial; crop

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.

农业现在是一个数据丰富的环境。大量近距离和远程传感器捕获农业生产系统的许多不同方面，特别是种植系统。如今，种植者能够记录和改变大多数农艺投入或操作的比率。然而，种植者很少使用他们所掌握的能力，因为他们无法将可用的数据流转化为信息，然后转化为良好的农艺决策。不正确的分析会产生不正确的决策。正因为如此，种植者在根据他们不太了解的信息进行决策时会非常谨慎。在作物模型中使用这些空间数据是一种明确的、潜在的非常重要的方式。作物模型对于农业界预测作物在不同情景下（替代管理和/或季节性气候变化的演变）的发展是非常宝贵的。虽然存在许多开发良好且具有良好信誉的作物模型，但这些模型都是建立在对一个点进行建模的假设之上的，这是一个田地或农场的平均响应。它们不是为高分辨率空间建模而设计的&通常在使用时会崩溃。本项目的目标是将点作物模型与空间数据相结合，为马铃薯生产生成有效的空间作物模型。这将有预测块茎大小分布（TSD）和管理TSD的各种驱动程序（环境和管理）的重点。通过为现有的作物模型提供空间信息，可以将种植者/农艺师直接从数据分析和决策中删除。专家知识将在作物模型中获取，但空间数据与最终用户之间没有直接参与，从而消除了错误和混淆的来源。因此，空间作物模型是一种空间数据融合和增值的方法，以原始的空间数据。该模型提供了一个相对简单的综合空间输出（推荐的可变速率管理操作），种植者可以获得采用。建模还允许估计不确定性（以及操作），以帮助种植者进行差异化管理的风险评估。从学术角度来看，需要研究和开发一些问题来实现这一目标。这些措施包括：1）填补马铃薯田作物变异量（幅度和空间结构）的知识空白。可用的空间研究非常少&需要这些信息来正确地在合理的边界限制内参数化任何空间模型。2）了解所观察到的作物产量变化的驱动因素。观察到的变异性可以与土壤和天气变化的空间信息以及管理决策联系起来。这有助于告知产量决定因素的空间模型。3）空间元模型的开发。空间裁剪模型依赖于现有点裁剪模型的输出，该输出用作空间元模型的输入。空间元模型是一个新概念。它需要输入的标准化，特别是在空间足迹方面，正确的邻里相互作用参数化以及空间模型中每个点的不确定性的正确建模。正确的数据处理和上述1）和2）点的知识将确保元模型被正确设计和填充。该项目将在项目后期通过田间试验进行验证。该项目汇集了英国领先的马铃薯生产行业专业知识（SAC，SRUC，McCains）、供应链和加工（McCains）、马铃薯生产机械（格里姆）和精准农业服务（SE），以及精准农业领域的领先学术研究人员（纽卡斯尔大学）和作物建模（纽卡斯尔大学，Mylnefield研究服务）。这个财团有能力交付项目并满足行业的需求。

项目成果

Conceptual Spatial Crop Models for Potato Production

马铃薯生产的概念空间作物模型

• DOI: 10.1017/s2040470017000851
• 发表时间: 2017
• 期刊: Advances in Animal Biosciences
• 作者: Chen H