Combining Process Modelling and LAI Observations to Diagnose Winter Wheat Nitrogen Status and Forecast Yield

Abstract

Climate, nitrogen (N) and leaf area index (LAI) are key determinants of crop yield. N additions can enhance yield but must be managed efficiently to reduce pollution. Complex process models estimate N status by simulating soil-crop N interactions, but such models require extensive inputs that are seldom available. Through model-data fusion (MDF), we combine climate and LAI time-series with an intermediate-complexity model to infer leaf N and yield. The DALEC-Crop model was calibrated for wheat leaf N and yields across field experiments covering N applications ranging from 0 to 200 kg N ha−1 in Scotland, UK. Requiring daily meteorological inputs, this model simulates crop C cycle responses to LAI, N and climate. The model, which includes a leaf N-dilution function, was calibrated across N treatments based on LAI observations, and tested at validation plots. We showed that a single parameterization varying only in leaf N could simulate LAI development and yield across all treatments—the mean normalized root-mean-square-error (NRMSE) for yield was 10%. Leaf N was accurately retrieved by the model (NRMSE = 6%). Yield could also be reasonably estimated (NRMSE = 14%) if LAI data are available for assimilation during periods of typical N application (April and May). Our MDF approach generated robust leaf N content estimates and timely yield predictions that could complement existing agricultural technologies. Moreover, EO-derived LAI products at high spatial and temporal resolutions provides a means to apply our approach regionally. Testing yield predictions from this approach over agricultural fields is a critical next step to determine broader utility.

The Effect of Antecedence on Empirical Model Forecasts of Crop Yield from Observations of Canopy Properties

“…Predictive models can be developed from these co-located data to describe field-based variations in yield. Process-based crop modelling [36,37], as opposed to the empirical modelling or machine learning used here, may also provide insights by linking production, developmental stage and allocation to rates of photosynthesis determined by conditions, LAI and leaf chlorophyll [35].…”

“…Process modelling connects climate to photosynthesis and crop development and represents the dynamic feedbacks between these, reducing the likelihood of over-fitting. Model calibration using time series of canopy observations means that model dynamics can be evaluated, and their forecast errors propagated [37]. For crop yields a key test is whether process model-data assimilation can explain yield variability mechanistically, for instance through differences in foliar traits such a leaf N content linked to soil N availability.…”

Research on Estimation Model for Cotton Canopy Structure Parameters Based on Hyperspectral Vegetation Index

“…the vertical profile of plant canopy, leaf area index (LAI) plays a key role in plant ecological and biophysical processes [11] and has been widely used for estimate foliage cover, crop growth and yield [12]. The Leaf Area Index (LAI) serves as a crucial indicator, reflecting crop canopy structure and growth [13,14].…”

“…Remote sensing technology enables timely, dynamic, and macroscopic monitoring, establishing itself as a crucial method for tracking crop growth information. Over recent years, numerous studies, both domestic and international, have employed remote sensing technology to investigate crop biomass [11,15,19]. Hyperspectral remote sensing serves not only to enhance the recognition of crop and vegetation types but also to monitor crop growth, retrieve physiochemical characteristics, diagnose nutrition status, extract crop canopy information, and estimate agronomic parameters and chemical components [8].…”

Evaluation of Landsat 9 and Sentinel 2 satellites in estimation of cotton leaf area index using M5 tree model

“…Cotton as a product has a historical record in Golestan province. Monitoring the leaf area index changes, can be a basis for the amount of cotton fertilization during the growing season (Revill et al, 2021;Shi et al, 2021). Fast and accurate monitoring of the cotton plant leaf area index is very important to determine the fertilization rate of this product.…”