Cocoa; future yields across West Africa

可可;

• 批准号: NE/S013598/1
• 负责人: Mathew Williams
• 金额: $ 16.7万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FS013598%2F1
• 关键词: Cocoa; future; yields; across; West

Seventy percent of the world cocoa production is produced by small holders in West Africa. However, yields are low and at risk from climate change, pests and diseases and many cocoa farming families live around or below the international poverty line. Major changes are needed to develop a sustainable supply chain. Being able to accurately predict cocoa yields and production will be one such change. Currently, accurate production forecasts are only available to a handful of large multinational cocoa companies and brokers, who run their proprietary forecasting programmes, based on physical monitoring of hundreds of cocoa farms. All other actors in the cocoa industry lack a reliable source of information about the expected harvest. However, such forecasts are essential to support planning and adaptation across the supply chain. They are also essential to price formation, which is determined in futures markets, which rely on participants' expectations of future harvests. Currently, information is asymmetrical in the market, favouring the few big players. Here we propose to use a demonstrated technology to generate accurate forecasting models at a fraction of the cost of the proprietary systems, making them affordable for a broad audience in the cocoa industry and hence contributing to the democratisation of the cocoa sector. We combine a range of data sources now available over long periods (20-40 years) with machine learning and models to generate a robust forecast tool. We now have time series mapping of critical determinants of yield, including rainfall, leaf area index, surface soil moisture, soil physical properties. We also have regional and national yield data. There is strong evidence of climate (soil moisture) impacts on yield, but little information on the critical thresholds in drought stress effects. Here we use process modelling to link plant production to soil moisture through the rooting zone. We calibrate our model at site scale to couple plant-soil processes, and then calibrate the model at regional and national scale to couple yield to plant-soil indicators. The outcome is a robust tool for generating forecasts of yield through the year, with clear confidence intervals. This information provides the novel input for the cocoa value chain.The output from this research will form the scientific basis on which commercial providers can offer products and services to end-users in the cocoa industry, as demonstrated by two project partners. Once proven for cocoa, the approach can be replicated in a range of other crops, from grains to tropical commodities to vegetable oils.

世界上有70％的可可生产是由西非的小持有人生产的。但是，收益率低，气候变化，害虫和疾病以及许多可可养殖家庭生活在国际贫困线附近或之下。需要重大变化来发展可持续的供应链。能够准确预测可可的产量和生产将就是这样的变化。当前，基于数百种可可农场的物理监测，仅适用于少数大型跨国可可公司和经纪人，这些预测的准确生产预测仅适用于经营其专有预测计划。可可行业的所有其他参与者都缺乏有关预期收获的可靠信息来源。但是，这种预测对于支持整个供应链的计划和适应至关重要。它们对于在期货市场中确定的价格形成也是必不可少的，这取决于参与者对未来收获的期望。目前，信息在市场上是不对称的，偏爱少数大型参与者。在这里，我们建议使用一项演示的技术以专有系统成本的一小部分生成准确的预测模型，从而使它们负担得起可可行业的广泛受众，从而为可可行业的民主化做出了贡献。我们将一系列数据源结合在一起，现在长期（20 - 40年）与机器学习和模型相结合，以生成强大的预测工具。现在，我们拥有关键产量决定因素的时间序列映射，包括降雨，叶子面积指数，表面土壤水分，土壤物理特性。我们也有区域和国家收益数据。有强有力的证据表明气候（土壤水分）对产量产生影响，但是关于干旱压力影响的关键阈值的信息很少。在这里，我们使用工艺建模将植物生产与通过生根区域的土壤水分联系起来。我们以现场尺度校准模型，以对植物土壤工艺进行对，然后在区域和国家规模上校准该模型，以将其屈服与植物土壤指标相结合。结果是一个强大的工具，可在一年中以明确的置信区间产生产量的预测。该信息为可可价值链提供了新的输入。这项研究的输出将构成科学基础，商业提供商可以为可可行业的最终用户提供产品和服务，如两个项目合作伙伴所证明的那样。一旦证明可可，该方法就可以在其他农作物中复制，从谷物到热带商品再到植物油。

项目成果

Characterizing the Error and Bias of Remotely Sensed LAI Products: An Example for Tropical and Subtropical Evergreen Forests in South China

表征遥感 LAI 产品的误差和偏差：以华南热带和亚热带常绿森林为例

• DOI: 10.3390/rs12193122
• 发表时间: 2020
• 期刊: Remote Sensing
• 影响因子: 5
• 作者: Zhao Yuan;Chen Xiaoqiu;Smallman Thomas Luke;Flack-Prain Sophie;Milodowski David T.;Williams Mathew
• 通讯作者: Williams Mathew

The impact of climate change and climate extremes on sugarcane production

• DOI: 10.1111/gcbb.12797
• 发表时间: 2021-01-19
• 期刊: GLOBAL CHANGE BIOLOGY BIOENERGY
• 影响因子: 5.6
• 作者: Flack-Prain, Sophie;Shi, Liangsheng;Williams, Mathew
• 通讯作者: Williams, Mathew

The Effect of Antecedence on Empirical Model Forecasts of Crop Yield from Observations of Canopy Properties

• DOI: 10.3390/agriculture11030258
• 发表时间: 2021-03
• 期刊: Agriculture
• 作者: Anna Florence;A. Revill;S. Hoad;R. Rees;M. Williams