Impact of spatial resolution on the quality of crop yield predictions for site-specific crop management

Al-Shammari, Dhahi; Whelan, Brett; Wang, Chen; Bramley, R. G. V.; Fajardo, Mario; Bishop, Thomas

doi:10.1016/j.agrformet.2021.108622

Cited by 10 publications

(2 citation statements)

References 43 publications

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“…This shows that the scaling may have a significant effect and can be complex because some variables can be overestimated at certain scales. Thus, a comparison between different aggregation scales could be a good approach to crop model evaluation (Al-Shammari et al, 2021).…”

Section: Scaling Errorsmentioning

confidence: 99%

A review of methods to evaluate crop model performance at multiple and changing spatial scales

et al. 2022

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Crop models are useful tools because they can help understand many complex processes by simulating them. They are mainly designed at a specific spatial scale, the field. But with the new spatial data being made available in modern agriculture, they are being more and more applied at multiple and changing scales. These applications range from typically at broader scales, to perform regional or national studies, or at finer scales to develop modern site-specific management approaches. These new approaches to the application of crop models raise new questions concerning the evaluation of their performance, particularly for downscaled applications. This article first reviews the reasons why practitioners decide to spatialize crop models and the main methods they have used to do this, which questions the best place of the spatialization process in the modelling framework. A strong focus is then given to the evaluation of these spatialized crop models. Evaluation metrics, including the consideration of dedicated sensitivity indices are reviewed from the published studies. Using a simple example of a spatialized crop model being used to define management zones in precision viticulture, it is shown that classical model evaluation involving aspatial indices (e.g. the RMSE) is not sufficient to characterize the model performance in this context. A focus is made at the end of the review on potentialities that a complementary evaluation could bring in a precision agriculture context.

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Section: Scaling Errorsmentioning

confidence: 99%

A review of methods to evaluate crop model performance at multiple and changing spatial scales

et al. 2022

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“…For example, in [62] researchers focus on the DNGP, a webbased RS system, which not only helps to reduce planting costs, operating costs and environmental costs, but also increases the yield to some extent; in [63][64][65][66][67] researchers mainly focus on Site-specific Management (SSM). SSM originated from PA in the 1980s, which means that soils or crops with the same characteristics will be treated similarly [68][69][70][71][72]. SSM technologies include Yield Mapping (YM), the GIS, and the GNSS.…”

Section: Introductionmentioning

confidence: 99%

Digital Agriculture: Mapping Knowledge Structure and Trends

Zhou,

Yin

2023

IEEE Access

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With the advancement of digital technologies, digital agriculture improves agricultural efficiency and sustainability, reducing resource waste and environmental burden. Hence, DA has emerged as a new catalyst for grain productivity and a driving force behind climate improvement, with broad prospects. However, digital agriculture is still in its early stage in most countries and there are few systematic reviews. In China, for example, there were only 3 non-core reviews out of 817 literatures on DA. Meanwhile, the use of Citespace is revolutionary as it leverages data visualization to uncover hidden patterns and relationships within literatures, aiding researchers in achieving their objectives. This study aims to address this research gap by using Citespace to produce a non-subjective and testable review on DA. In this study, we collected 2264 literatures by retrieving the WoS database in the timespan 1997-2022. The results show that (1) over time, the annual number of publications on DA has gradually increased and the research can be roughly divided into three stages: start-up, steady development, and rapid development stage; (2) research streams of DA can be further divided into six categories: Remote Sensing, Climate-Smart Agriculture, Artificial Intelligence, Internet of Things, Big Data and System Integration; (3) development of research frontiers could be divided into three parts: "exploration of digital agriculture technologies", "operation management of digital agriculture" and "limitations of digital agriculture"; (4) future research should pay more attention to the innovation and scientific evaluation of digital technology and operation management. Also, effective DA policies should be put forward to provide a theoretical basis and decision-making reference.

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

Al-Shammari,

Chen,

Wimalathunge

et al. 2024

Precision Agric

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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.

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Impact of spatial resolution on the quality of crop yield predictions for site-specific crop management

Cited by 10 publications

References 43 publications

A review of methods to evaluate crop model performance at multiple and changing spatial scales

A review of methods to evaluate crop model performance at multiple and changing spatial scales

Digital Agriculture: Mapping Knowledge Structure and Trends

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

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