Effects of input data aggregation on simulated crop yields in temperate and Mediterranean climates

Maharjan, Ganga Ram; Hoffmann, Holger; Webber, Heidi; Srivastava, Amit Kumar; Weihermüller, Lutz; Villa, Ana; Coucheney, Elsa; Lewan, Elisabet; Trombi, Giacomo; Moriondo, Marco; Bindi, Marco; Grosz, Balázs; Dechow, René; Doro, Luca; Kersebaum, Kurt Christian; Stella, Tommaso; Specka, Xenia; Nendel, Claas; Constantin, Julie; Raynal, Hélène; Ewert, Frank; Gaiser, Thomas

doi:10.1016/j.eja.2018.11.001

Cited by 18 publications

(12 citation statements)

References 39 publications

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“…Table 5 lists the mean absolute percentage error (MAPE) results of simulated vs. measured total aboveground biomass per site. The standard run produced the highest MAPE in seven out of the fourteen sites (2,4,6,7,8,13,14), all of the CC assimilation approaches in five sites (1,3,9,11,12), and the EM CC approach in one site (5). The results for site 10 show an equal performance of the standard run and the WM CC approach.…”

Section: Results Of Root Mean Squared Error (Rmse) Analysismentioning

confidence: 87%

“…In six sites, the highest RMSE was produced by the standard run (sites 2, 4, 6, 7, 12, 13, 14), in five sites by either of the two CC assimilation approaches (sites 1,8,9,10,11) and in one site by the EM SCC approach only (site 5). In five sites, the lowest RMSE was produced by the WM SCC approach (sites 4, 5, 6,8,9) and by the EnKF SCC approach, respectively (sites 2, 10, 11, 12, 14), in two sites by EM SCC (sites 1, 3) and WM CC approaches respectively (sites 7, 13). Minimum RMSE values among all sites ranged between 0.89 t ha −1 (WM SSC, Site 9) and 3.35 t ha −1 (WM SSC, Site 4), maximum values between 3.77 t ha −1 (Site 9, EnKF CC) and 7.35 t ha −1 (Site 2, Standard Run).…”

Section: Evaluation Of Model Performancementioning

confidence: 98%

“…The detailed spatial characteristics of those inputs, however, are often difficult to measure and thus generalized or unknown [2]. Even if available, the resolution of the available data affects the results of the simulation and needs to be taken into consideration when interpreting the results [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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New Approaches for the Assimilation of LAI Measurements into a Crop Model Ensemble to Improve Wheat Biomass Estimations

et al. 2020

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The assimilation of LAI measurements, repeatedly taken at sub-field level, into dynamic crop simulation models could provide valuable information for precision farming applications. Commonly used updating methods such as the Ensemble Kalman Filter (EnKF) rely on an ensemble of model runs to update a limited set of state variables every time a new observation becomes available. This threatens the model’s integrity, as not the entire table of model states is updated. In this study, we present the Weighted Mean (WM) approach that relies on a model ensemble that runs from simulation start to simulation end without compromising the consistency and integrity of the state variables. We measured LAI on 14 winter wheat fields across France, Germany and the Netherlands and assimilated these observations into the LINTUL5 crop model using the EnKF and WM approaches, where the ensembles were created using one set of crop component (CC) ensemble generation variables and one set of soil and crop component (SCC) ensemble generation variables. The model predictions for total aboveground biomass and grain yield at harvest were evaluated against measurements collected in the fields. Our findings showed that (a) the performance of the WM approach was very similar to the EnKF approach when SCC variables were used for the ensemble generation, but outperformed the EnKF approach when only CC variables were considered, (b) the difference in site-specific performance largely depended on the choice of the set of ensemble generation variables, with SCC outperforming CC with regard to both biomass and grain yield, and (c) both EnKF and WM improved accuracy of biomass and yield estimates over standard model runs or the ensemble mean. We conclude that the WM data assimilation approach is equally efficient to the improvement of model accuracy, compared to the updating methods, but it has the advantage that it does not compromise the integrity and consistency of the state variables.

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Section: Results Of Root Mean Squared Error (Rmse) Analysismentioning

confidence: 87%

Section: Evaluation Of Model Performancementioning

confidence: 98%

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New Approaches for the Assimilation of LAI Measurements into a Crop Model Ensemble to Improve Wheat Biomass Estimations

et al. 2020

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“…Crop growth models, which are widely used in crop yield forecasting, can accurately simulate crop yield at the field scale by explaining the relationships of crop growth with soil, cultivation management, atmospheric, or other factors [6][7][8][9]. Statistical models, which construct a statistical relationship between crop influence factors and yields, are major tools in estimating crop yield [10].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Maize Yield at the City Level in China Using Multi-Source Data

et al. 2021

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Maize is a widely grown crop in China, and the relationships between agroclimatic parameters and maize yield are complicated, hence, accurate and timely yield prediction is challenging. Here, climate, satellite data, and meteorological indices were integrated to predict maize yield at the city-level in China from 2000 to 2015 using four machine learning approaches, e.g., cubist, random forest (RF), extreme gradient boosting (Xgboost), and support vector machine (SVM). The climate variables included the diffuse flux of photosynthetic active radiation (PDf), the diffuse flux of shortwave radiation (SDf), the direct flux of shortwave radiation (SDr), minimum temperature (Tmn), potential evapotranspiration (Pet), vapor pressure deficit (Vpd), vapor pressure (Vap), and wet day frequency (Wet). Satellite data, including the enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and adjusted vegetation index (SAVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), were used. Meteorological indices, including growing degree day (GDD), extreme degree day (EDD), and the Standardized Precipitation Evapotranspiration Index (SPEI), were used. The results showed that integrating all climate, satellite data, and meteorological indices could achieve the highest accuracy. The highest estimated correlation coefficient (R) values for the cubist, RF, SVM, and Xgboost methods were 0.828, 0.806, 0.742, and 0.758, respectively. The climate, satellite data, or meteorological indices inputs from all growth stages were essential for maize yield prediction, especially in late growth stages. R improved by about 0.126, 0.117, and 0.143 by adding climate data from the early, peak, and late-period to satellite data and meteorological indices from all stages via the four machine learning algorithms, respectively. R increased by 0.016, 0.016, and 0.017 when adding satellite data from the early, peak, and late stages to climate data and meteorological indices from all stages, respectively. R increased by 0.003, 0.032, and 0.042 when adding meteorological indices from the early, peak, and late stages to climate and satellite data from all stages, respectively. The analysis found that the spatial divergences were large and the R value in Northwest region reached 0.942, 0.904, 0.934, and 0.850 for the Cubist, RF, SVM, and Xgboost, respectively. This study highlights the advantages of using climate, satellite data, and meteorological indices for large-scale maize yield estimation with machine learning algorithms.

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“…(http://www.scale-it.net/). Ее задачакоординировать европейские исследования по использованию моделей продуктивности для решения практических задач поддержки решений в различном пространственном масштабе (50). Сфера деятельности этой группы включает организацию проектов ансамблевых расчетов, агрегацию специфических входных данных с разных географических локаций, научное обоснование методов пространственной дискретизации и пространственной интерполяции входных данных для моделей с оценкой степени неопределенности получаемых результатов, сбор информации о персоналиях, моделях, подходах, предоставление оперативной информации о вызовах и достижениях в обсуждаемой области исследований.…”

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ОПЕРАТИВНОЕ И ДОЛГОСРОЧНОЕ ПРОГНОЗИРОВАНИЕ ПРОДУКТИВНОСТИ ПОСЕВОВ НА ОСНОВЕ МАССОВЫХ РАСЧЕТОВ ИМИТАЦИОННОЙ МОДЕЛИ АГРОЭКОСИСТЕМЫ В ГЕОИНФОРМАЦИОННОЙ СРЕДЕ (Обзор)

Якушев¹,

Якушев²,

Баденко³

et al. 2020

S-h. biol.

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Системы земледелия, представляющие собой комплекс взаимосвязанных агротехнических, мелиоративных и организационных мероприятий, обеспечивающих эффективное использование агроландшафтов, сохранение и повышение плодородия почвы, а также получение высоких урожаев, рассматриваются как инструмент управления сельскохозяйственным производством, особенно актуальный в современных изменяющихся социально-экономических и природно-климатических условиях. Оптимизация таких систем в значительной степени строится на расчетах числовых значений параметров агроэкосистем и прогнозировании их динамики с применением математических моделей. В растениеводстве получили развитие статистические и динамические имитационные прогнозные модели. Последние модели более точные, адаптивные и позволяют описать развитие агроэкосистем при нестабильных климатических условиях и под воздействием различных агротехнических мероприятий. Математические модели широко обсуждаются в научной литературе по экологии, почвоведению, растениеводству. Основные проблемы регионального планирования систем земледелия на средне-и долгосрочную перспективу могут быть решены на основе моделирования в среде геоинформационных систем. Обзор подходов к прогнозированию продуктивности посевов на основе массовых расчетов по имитационной модели агроэкосистемы в геоинформационной среде позволяет определить, как можно их использовать для обоснования систем земледелия. При этом по пространственному охвату методы моделирования делятся на макромасштабные, мезомасштабные и микромасштабные. В общем случае для разных масштабов используются неодинаковые подходы. Для создания адекватной этим подходам универсальной среды массовых расчетов по динамическим моделям агроэкосистем для разных уровней пространственного охвата предлагается использовать соответствующие компьютерные оболочки для поливариантных расчетов-универсальную среду моделирования, для которой представлены требования для проведения расчетов по разным моделям от различных поставщиков. Решение проблемы формирования набора точек для расчета по модели состоит в том, что они должны находиться на возделываемых сельхозугодиях и адекватно представлять почвенные и климатические условия региона. Ключевые слова: агроэкосистемы, имитационное моделирование, массовые вычисления, прогнозирование, геоинформационные системы, системы земледелия.

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Effects of input data aggregation on simulated crop yields in temperate and Mediterranean climates

Cited by 18 publications

References 39 publications

New Approaches for the Assimilation of LAI Measurements into a Crop Model Ensemble to Improve Wheat Biomass Estimations

New Approaches for the Assimilation of LAI Measurements into a Crop Model Ensemble to Improve Wheat Biomass Estimations

Prediction of Maize Yield at the City Level in China Using Multi-Source Data

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