Incorporation of mechanistic model outputs as features for data-driven models for yield prediction: a case study on wheat and chickpea

IF 5.4 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Dhahi Al-Shammari, Yang Chen, Niranjan S. Wimalathunge, Chen Wang, Si Yang Han, Thomas F. A. Bishop
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引用次数: 0

Abstract

Introduction

Context Data-driven models (DDMs) are increasingly used for crop yield prediction due to their ability to capture complex patterns and relationships. DDMs rely heavily on data inputs to provide predictions. Despite their effectiveness, DDMs can be complemented by inputs derived from mechanistic models (MMs).

Methods

This study investigated enhancing the predictive quality of DDMs by using as features a combination of MMs outputs, specifically biomass and soil moisture, with conventional data sources like satellite imagery, weather, and soil information. Four experiments were performed with different datasets being used for prediction: Experiment 1 combined MM outputs with conventional data; Experiment 2 excluded MM outputs; Experiment 3 was the same as Experiment 1 but all conventional temporal data were omitted; Experiment 4 utilised solely MM outputs. The research encompassed ten field-years of wheat and chickpea yield data, applying the eXtreme Gradient Boosting (XGBOOST) algorithm for model fitting. Performance was evaluated using root mean square error (RMSE) and the concordance correlation coefficient (CCC).

Results and conclusions

The validation results showed that the XGBOOST model had similar predictive power for both crops in Experiments 1, 2, and 3. For chickpeas, the CCC ranged from 0.89 to 0.91 and the RMSE from 0.23 to 0.25 t ha−1. For wheat, the CCC ranged from 0.87 to 0.92 and the RMSE from 0.29 to 0.35 t ha−1. However, Experiment 4 significantly reduced the model's accuracy, with CCCs dropping to 0.47 for chickpeas and 0.36 for wheat, and RMSEs increasing to 0.46 and 0.65 t ha−1, respectively. Ultimately, Experiments 1, 2, and 3 demonstrated comparable effectiveness, but Experiment 3 is recommended for achieving similar predictive quality with a simpler, more interpretable model using biomass and soil moisture alongside non-temporal conventional features.

Abstract Image

将机理模型输出结果作为数据驱动模型的特征纳入产量预测:关于小麦和鹰嘴豆的案例研究
引言数据驱动模型(DDM)能够捕捉复杂的模式和关系,因此越来越多地用于作物产量预测。DDM 主要依靠数据输入来提供预测。尽管 DDM 非常有效,但仍可通过机理模型(MMs)的输入进行补充。本研究通过将 MMs 输出(特别是生物量和土壤湿度)与卫星图像、天气和土壤信息等传统数据源相结合,研究如何提高 DDM 的预测质量。利用不同的数据集进行了四次预测实验:实验 1 结合了 MM 输出和常规数据;实验 2 排除了 MM 输出;实验 3 与实验 1 相同,但省略了所有常规时间数据;实验 4 仅使用 MM 输出。研究涵盖了十个田间年的小麦和鹰嘴豆产量数据,采用了梯度提升算法(XGBOOST)进行模型拟合。验证结果表明,在实验 1、2 和 3 中,XGBOOST 模型对两种作物具有相似的预测能力。鹰嘴豆的 CCC 为 0.89 至 0.91,RMSE 为 0.23 至 0.25 吨/公顷。小麦的 CCC 为 0.87 至 0.92,RMSE 为 0.29 至 0.35 吨/公顷。然而,实验 4 大大降低了模型的准确性,鹰嘴豆和小麦的 CCC 分别降至 0.47 和 0.36,RMSE 分别增至 0.46 和 0.65 t ha-1。最终,实验 1、2 和 3 的效果相当,但建议进行实验 3,利用生物量和土壤水分以及非时间性常规特征,通过更简单、更易解释的模型实现类似的预测质量。
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来源期刊
Precision Agriculture
Precision Agriculture 农林科学-农业综合
CiteScore
12.30
自引率
8.10%
发文量
103
审稿时长
>24 weeks
期刊介绍: Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.
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