Development of a mobile computing framework to aid decision-making on organic fertilizer management using a crop growth model

Abstract. The well-established dynamical global vegetation, hydrology, and crop growth model LPJmL is extended with a terrestrial nitrogen cycle to account for nutrient limitations. In particular, processes of soil nitrogen dynamics, plant uptake, nitrogen allocation, response of photosynthesis and maintenance respiration to varying nitrogen concentrations in plant organs, and agricultural nitrogen management are included in the model. All new model features are described in full detail and the results of a global simulation of the historic past (1901–2009) are presented for evaluation of the model performance. We find that the implementation of nitrogen limitation significantly improves the simulation of global patterns of crop productivity. Regional differences in crop productivity, which had to be calibrated via a scaling of the maximum leaf area index, can now largely be reproduced by the model, except for regions where fertilizer inputs and climate conditions are not the yield-limiting factors. Furthermore, it can be shown that land use has a strong influence on nitrogen losses, increasing leaching by 93 %.

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An efficient and accurate method for obtaining regional scale rice growth conditions based on WOFOST model and satellite images

10.5194/egusphere-egu21-10688 ◽

2021 ◽

Author(s):

Bingyu Zhao ◽

Meiling Liu ◽

Jiianjun Wu ◽

Xiangnan Liu ◽

Mengxue Liu ◽

...

Keyword(s):

Remote Sensing ◽

Data Assimilation ◽

Growth Model ◽

Growth Parameters ◽

Remote Sensing Data ◽

Growth Conditions ◽

Crop Growth ◽

Crop Growth Model ◽

Parallel Method ◽

Rice Growth

<p>It is very important to obtain regional crop growth conditions efficiently and accurately in the agricultural field. The data assimilation between crop growth model and remote sensing data is a widely used method for obtaining vegetation growth information. This study aims to present a parallel method based on graphic processing unit (GPU) to improve the efficiency of the assimilation between RS data and crop growth model to estimate rice growth parameters. Remote sensing data, Landsat and HJ-1 images were collected and the World Food Studies (WOFOST) crop growth model which has a strong flexibility was employed. To acquire continuous regional crop parameters in temporal-spatial scale, particle swarm optimization (PSO) data assimilation method was used to combine remote sensing images and WOFOST and this process is accompanied by a parallel method based on the Compute Unified Device Architecture (CUDA) platform of NVIDIA GPU. With these methods, we obtained daily rice growth parameters of Zhuzhou City, Hunan, China and compared the efficiency and precision of parallel method and non-parallel method. Results showed that the parallel program has a remarkable speedup (reaching 240 times) compared with the non-parallel program with a similar accuracy. This study indicated that the parallel implementation based on GPU was successful in improving the efficiency of the assimilation between RS data and the WOFOST model and was conducive to obtaining regional crop growth conditions efficiently and accurately.</p>

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Coupling a land-surface model with a crop growth model to improve ET flux estimations in the Upper Ganges basin, India

Hydrology and Earth System Sciences ◽

10.5194/hess-18-4223-2014 ◽

2014 ◽

Vol 18 (10) ◽

pp. 4223-4238 ◽

Cited By ~ 12

Author(s):

G. M. Tsarouchi ◽

W. Buytaert ◽

A. Mijic

Keyword(s):

Growth Model ◽

Land Surface ◽

Coupled Model ◽

Crop Growth ◽

Original Model ◽

Surface Model ◽

Strong Impact ◽

Crop Growth Model ◽

Mean Error ◽

The Impact

Abstract. Land-Surface Models (LSMs) are tools that represent energy and water flux exchanges between land and the atmosphere. Although much progress has been made in adding detailed physical processes into these models, there is much room left for improved estimates of evapotranspiration fluxes, by including a more reasonable and accurate representation of crop dynamics. Recent studies suggest a strong land-surface–atmosphere coupling over India and since this is one of the most intensively cultivated areas in the world, the strong impact of crops on the evaporative flux cannot be neglected. In this study we dynamically couple the LSM JULES with the crop growth model InfoCrop. JULES in its current version (v3.4) does not simulate crop growth. Instead, it treats crops as natural grass, while using prescribed vegetation parameters. Such simplification might lead to modelling errors. Therefore we developed a coupled modelling scheme that simulates dynamically crop development and parametrized it for the two main crops of the study area, wheat and rice. This setup is used to examine the impact of inter-seasonal land cover changes in evapotranspiration fluxes of the Upper Ganges River basin (India). The sensitivity of JULES with regard to the dynamics of the vegetation cover is evaluated. Our results show that the model is sensitive to the changes introduced after coupling it with the crop model. Evapotranspiration fluxes, which are significantly different between the original and the coupled model, are giving an approximation of the magnitude of error to be expected in LSMs that do not include dynamic crop growth. For the wet season, in the original model, the monthly Mean Error ranges from 7.5 to 24.4 mm month−1, depending on different precipitation forcing. For the same season, in the coupled model, the monthly Mean Error's range is reduced to 5.4–11.6 mm month−1. For the dry season, in the original model, the monthly Mean Error ranges from 10 to 17 mm month−1, depending on different precipitation forcing. For the same season, in the coupled model, the monthly Mean Error's range is reduced to 2.2–3.4 mm month−1. The new modelling scheme, by offering increased accuracy of evapotranspiration estimations, is an important step towards a better understanding of the two-way crops–atmosphere interactions.

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