Remote sensing and on-farm experiments for determining in-season nitrogen rates in winter wheat – Options for implementation, model accuracy and remaining challenges

Piikki, Kristin; Stadig, H.

doi:10.1016/j.fcr.2022.108742

Cited by 7 publications

(1 citation statement)

References 35 publications

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“…Remote sensing is increasingly being used as a timely and nondestructive tool for mapping the N nutrition status of plants and to rapidly assess the spatial variability within a field based on the canopy reflectance. In-season measurements of the crop N status, linked with local production information, offer promise for fine-tuning N fertilisation rates and are increasingly being used for maize and wheat [19]. A drawback of these approaches is the need for an expected yield.…”

Section: Introductionmentioning

confidence: 99%

Development of Algorithm for Determining N Fertiliser Requirements of Winter Wheat Based on N Status Using APSIM Modelling

Vogeler,

Kumar,

Knudsen

et al. 2024

Crops

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The determination of optimum nitrogen (N) fertilisation rates, which maximise yields and minimise N losses, remains problematic due to unknown upcoming crop requirements and near-future supply by the soil. Remote sensing can be used for determining the crop N status and to assess the spatial variability within a field or between fields. This can be used to improve N fertilisation, provided that the optimal fertilisation rate at the time of fertiliser application for an expected yield is known. Using the APSIM-wheat model, we developed an algorithm that relates the N status of the plants at early development stages to the yield response to N. Simulations were performed for winter wheat under growth conditions in Denmark. To obtain a range of different N status in the biomass at early growth stages, the soil N in autumn was varied from 20 to 180 kg N ha−1, and at BBCH23, fertiliser was applied at a rate of 50 kg N ha−1. In a full factorial setup, additional N fertiliser was applied ranging from 0 to 150 kg N ha−1 during three different development stages (BBCH30, 32, and 37). The algorithm was evaluated by comparing model outputs with a standard N application of 50 kg N ha−1 at BBCH23 and 150 kg N ha−1 at BBCH30. The evaluation showed that, depending on the N status of the soil, the algorithm either provided higher or lower optimal N fertilisation rates when targeting 95% of the maximum yield, and these affected the grain yield and the grain N, as well as the amount of N leaching. Split application of fertiliser into three applications was generally beneficial, with decreased product-related N leaching of up to nearly 30%. Further testing of the model under different environmental conditions is needed before such an algorithm can be used to guide N fertilisation.

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Section: Introductionmentioning

confidence: 99%

Development of Algorithm for Determining N Fertiliser Requirements of Winter Wheat Based on N Status Using APSIM Modelling

Vogeler,

Kumar,

Knudsen

et al. 2024

Crops

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UAV-based canopy monitoring: calibration of a multispectral sensor for green area index and nitrogen uptake across several crops

Bukowiecki,

Rose,

Holzhauser

et al. 2024

Precision Agric

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The fast and accurate provision of within-season data of green area index (GAI) and total N uptake (total N) is the basis for crop modeling and precision agriculture. However, due to rapid advancements in multispectral sensors and the high sampling effort, there is currently no existing reference work for the calibration of one UAV (unmanned aerial vehicle)-based multispectral sensor to GAI and total N for silage maize, winter barley, winter oilseed rape, and winter wheat.In this paper, a practicable calibration framework is presented. On the basis of a multi-year dataset, crop-specific models are calibrated for the UAV-based estimation of GAI throughout the entire growing season and of total N until flowering. These models demonstrate high accuracies in an independent evaluation over multiple growing seasons and trial sites (mean absolute error of 0.19–0.48 m2 m−2 for GAI and of 0.80–1.21 g m−2 for total N). The calibration of a uniform GAI model does not provide convincing results. Near infrared-based ratios are identified as the most important component for all calibrations. To account for the significant changes in the GAI/ total N ratio during the vegetative phase of winter barley and winter oilseed rape, their calibrations for total N must include a corresponding factor. The effectiveness of the calibrations is demonstrated using three years of data from an extensive field trial. High correlation of the derived total N uptake until flowering and the whole-season radiation uptake with yield data underline the applicability of UAV-based crop monitoring for agricultural applications.

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In-season dynamic diagnosis of maize nitrogen status across the growing season by integrating proximal sensing and crop growth modeling

Dong,

Miao,

Wang

et al. 2024

Computers and Electronics in Agriculture

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Remote sensing and on-farm experiments for determining in-season nitrogen rates in winter wheat – Options for implementation, model accuracy and remaining challenges

Cited by 7 publications

References 35 publications

Development of Algorithm for Determining N Fertiliser Requirements of Winter Wheat Based on N Status Using APSIM Modelling

Development of Algorithm for Determining N Fertiliser Requirements of Winter Wheat Based on N Status Using APSIM Modelling

UAV-based canopy monitoring: calibration of a multispectral sensor for green area index and nitrogen uptake across several crops

In-season dynamic diagnosis of maize nitrogen status across the growing season by integrating proximal sensing and crop growth modeling

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